dataesr/scientific-paragraphs-categorization huggingface dataset:
Dynamic moss There are no data sets showing the pan-Arctic distribution of mosses. In addition in a changing climate the distribution of moss may also change. Therefore, moss was implemented in JULES so that it can be run either with a static map that is input at the start of the run or with dynamic growth determined by environmental conditions in the model. In order to determine the presence of moss in any grid cell, the model takes account of the local temperature, moisture, light, snow cover and, in some cases, wind speed (see Table  1 ). The moss cover is then determined by a "health" variable, whose value is updated once a day depending on the conditions over the past 24 h. Good conditions add to health and bad conditions subtract from it. It is contrained within bounds that result in maximum health within a year given optimum growing conditions.
This value was pragmatically chosen given that moss can have around 50-100 % cover in forests  (Beringer et al. , 2001). Moss cover is assumed to be 0 for the urban or ice fraction of a grid box. The relationship of moss health to moss cover would benefit from further calibration. Figure  2  shows a moss distribution simulated by JULES in the northern high latitudes and compares it with data from the  Euskirchen et al. (2007)  land-cover map. In general the most densely moss-covered areas correspond to the tundra land-cover classes, which are shown in bright green. Our scheme gives some general representation of this low vegetation cover, which is otherwise missing in JULES. Moss also grows in the boreal forest (shown in purple on the Euskirchen map), but in JULES it does not grow in the deciduous needleleaf zone, which may require some investigation.
Historical soil temperatures The Russian historical soil temperature data set is described in  Frauenfeld et al. (2004). Soil temperatures were measured at 242 stations, over different time periods starting as early as 1890. The measurements used in this paper were taken at depths of 0. 2, 0. 4, 0. 8, 1. 6 and 3. 2 m using extraction thermometers, with additional measurements at 0. 6, 1. 2 and 2. 4 m. At some of the sites the natural vegetation cover was removed and at others there is some possibility of site disturbance, however the majority of these measurement sites retained their natural vegetation and snow cover. International Polar Year Thermal State of Permafrost (IPY-TSP) borehole inventory data were compiled in 2007-2009 from both new and existing boreholes, achieving a wide spatial coverage of soil temperature data  (Romanovsky, 2010).
Simulation set-up For the historical period, JULES was driven by the WFDEI reanalysis data at a resolution of 0. 5 • (Sect. 2. 4. 1). JULES was driven by precipitation (sum of rain and snow in WFDEI), which was converted internally within the model to rain and snow. This maintains consistency with the future driving data. The model was spun up for 60 years by repeating the first 10 years of driving data  (1979) (1980) (1981) (1982) (1983) (1984) (1985) (1986) (1987) (1988)  and then run over the period 1979-2009 for the historical runs. After spin-up, the soil temperature and moisture contents were fully stabilised at the vast majority of points, i. e. there was no residual model drift. The future runs began at the start of 2006, taking their initial state from 2006 in the historical simulations and simulating both RCP4.
5 and RCP8. 5 scenarios until 2100. In order to capture all the main permafrost regions in the Northern Hemisphere, the simulations were run for the region north of 25 •. The mineral soil properties, land-cover fractions and topographic index data (needed for LSH, see Sect. 2. 1) were taken from HadGEM2-ES ancillary data  (Collins et al. , 2008). Organic soil ancillaries were generated using the method described in  Chadburn et al. (2015) , with the spatial distribution as described in Sect. 2. 3. 1. The simulations carried out are given in Table  2. This includes the standard JULES set-up (min4l), a higherresolution soil column (min14l), a deeper soil column (minD), the effects of moss and organic soils both separately and in combination (minmossD, orgD, orgmossD) and finally the modified snow scheme (orgmossDS).
, 2011) , and the observed soil temperatures vary greatly from around -1 to -7 • C as a result of various influences including land cover and snow  (Burn and Kokelj, 2009). This is a subject for future investigation. The active layer thickness is determined by both the annual cycle of soil temperatures and the thermal offset between the air and the soil. Table  3  compares these dynamics in JULES with observations from the IPY-TSP data set and cold sites from the Russian soil temperature data set (see Sects. 2. 4. 4 and 2. 6). The root mean squared error (RMSE) is calculated using the mean value of the metric for each site, so it quantifies the extent to which the variability between the sites is correctly simulated. In this table the most relevant values are the offset and attenuation of the annual cycle between the air and 1 m depth in the soil, since the soil surface is not so well defined in the observations.
Simulation Attenuation (fraction of amplitude) Offset ( duced annual cycle which matches better with the observations in Fig. 6c. There are still significant differences between the model and observations, shown by the size of the RMSE in Table  3. The error in winter snow depth when compared with the closest grid cells in the Globsnow data set (see Sect. 2. 4. 5) has a significant correlation of 0. 3 (for about 260 points) with the error in winter soil temperatures in orgmossDS, suggesting that snow explains at least some of the remaining error. Langer et al. (2013)  show that uncertainty in the simulated active layer depth comes from the uncertainty in soil composition, particularly ground-ice contents. Some variability is also expected when comparing a large grid cell with a point site, and this is difficult to quantify.
The correlation between the ALT in JULES and the percentage cover of permafrost from  (Brown et al. , 1998)  (100 % for continuous, 90 % for discontinuous, 50 % for sporadic and 10 % for isolated patches) is high, ranging between -0. 37 and -0. 51. However, there are still places where continuous permafrost is observed but JULES does not simulate permafrost. Figure  8  shows that in most of these areas, JULES simulates far too much snow, which will mean too much insulation in winter leading to soils that are too warm. This is particularly noticeable in north-east Canada and two areas in north-west Russia. In north-east Canada, however, it has been shown that the GlobSnow data set underestimates the SWE  (Langlois et al. , 2014) , so the over-estimation in JULES may not be as large as Fig. 8  suggests.
However, the permafrost in this region is unstable to thawing  (Thibault and Payette, 2009) , so a small bias in the model could make the difference between simulating permafrost or not. For most of the remaining land surface, JULES slightly underestimates the SWE. Hancock et al. (2014)  showed that JULES generally underestimates SWE when driven by reanalysis data sets. In the observations, the edge of the permafrost-affected zone corresponds quite closely with the 0 • isotherm, al- though there is a gap in western Russia (red lines in Fig. 7 ). In the JULES simulations this relationship is less consistent, suggesting more spatial heterogeneity in the relationship between air and soil temperatures. For example, excessive snow cover such as that seen in north-east Canada in Fig. 8  could contribute to this effect.
In areas where near-surface permafrost remains, there is a significant thickening of the active layer, which is relevant for consideration of the permafrost carbon feedback. Acknowledgements. The authors acknowledge financial support by the European Union Seventh Framework Programme (FP7/2007-2013) project PAGE21, under GA282700. E. Burke was supported by the joint UK DECC/Defra Met Office Hadley Centre Climate Programme GA01101. M. Langer is also supported by a post-doctoral research fellowship  (A. v. Humboldt Foundation). We also thank the reviewers for their comments to improve the manuscript. Edited by: R. Brown.
Introduction EVA (European virus Archive) is a non-profit organization, funded by the European Commission through the FP7 CAPACITIES Project GA n°228292. EVA mobilizes a European network of scientific laboratories with expertise in virological procedures to collect, characterize and distribute viruses and virus-derived products such as antibodies, antigens, diagnostic and research reagents and assay  [1]. As the coordinating partner of EVA, we have propagated a large number of virus strains, for worldwide distribution. Although ultra-low temperature storage at -80°C appears easy and cost effective, the potential problems involved in long-term preservation (i. e. the possibility of power/equipment failure) in the source laboratory, together with shipping to the customer (such as costs, packaging and cold chain preservation) need to be developed to the safest, highest, most robust and cost effective standards.
However, few experimentally-based data are available in the scientific literature to be applied easily for the development and the distribution of a large collection of viruses. In fact, specific protocols are required to reduce the damage induced by preservation techniques or storage conditions. This study focuses on optimizing both the protocol and the formulation for the lyophilization of enveloped and non-enveloped viruses possessing RNA or DNA genomes. One aim was to select the standardized method allowing optimal recovery of viral infectivity after one week at +4°C or +20°C, conditions mimicking transportation without cold chain preservation. The performance of the protocols is evaluated by measurement of the infectivity of the resulting freeze-dried material. Using a representative panel of viral specimens, several chemical stabilizers were assayed to evaluate the thermostability of the freeze-dried preparation.
Stabilizers Four different stabilizer solutions were tested in this study. For each, two concentrations were prepared depending on the data found in the literature. Sorbitol, trehalose and sucrose were obtained from Sigma (France). foetal bovine serum (FBS) was obtained from Invitrogen (France). For each assay, 100μl of infected cell culture supernatant was mixed with 100μl of stabilizer solution to obtain final concentrations at 5% and 10% with sorbitol and trehalose, at 3. 5% and 7% with sucrose, and at 25% and 50% with FBS.
Freeze drying of virus preparation Lyophilization was carried out in a BSL3 laboratory using a CHRIST alpha 1-4 LSC freezedryer. To avoid cross contamination and to ensure identical conditions, each freeze-dried batch production was performed on a single virus strain with different stabilizer formulations. Briefly, as described in section 3, 200μl of viral preparation were dispensed in sterile 2mL vials partially sealed with vented rubber stoppers before freezing in a dry ice/ethanol cooling bath. Two lyophilization programs were used for comparison; (i) LP1 "optimized;", this is used routinely in our laboratory and was previously developed based on a yellow fever 17 D vaccine virus model (data not shown), which consisted of a 6-hour primary drying step (1. 03 mbar, -20°C for 160 min then -10°C for 135 min) followed by a secondary drying step (0.
Virus titration and titre comparison Virus infectivity was quantified by estimating the 50% tissue culture infectivity dose (TCID 50 ) using standard cell culture procedures. Briefly, when cells reached 80% confluence in 96-well microtitre plates, six replicates were infected with 150μL of tenfold serial dilutions of the virus sample, and then incubated for 7 days at 37°C in a humidified atmosphere of 5% CO2. CPE was monitored using an inverted microscope, and infectivity titres were expressed as TCID 50 /ml based on the Karber formula. The virus infectivity was then compared and analyzed to determine the optimal sample formulation under the different conditions of preparation and storage. Moreover, to evaluate the program and temperature influence on viral infectivity independently of sample formulation, t-tests were used to estimate the difference between groups of data which were expressed as mean virus titres ± standard deviations (Table  2 ).
This work was supported in part by (i) the  European Virus Archive (European Union 7th Framework Programme  under grant agreement no. 228292 )(http://www. european-virus-archive. com/), (ii) the EDENext FP7-n°261504  EU  project and this paper is catalogued by the  EDENext Steering Committee  (http://www. edenext. eu),( iii) European Network for Diagnostics of "Imported" Viral Diseases (ENIVD)  funded from the  European Centre for Disease Prevention and Control  (http://www. enivd. de/index. htm). The work of RNC was done under the frame of EurNegVec  COST Action   TD1303. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Baseline examination (HALS-1) In 1984/1985, the Health and Lifestyle Survey (HALS-1), was conducted in a random sample of the Population of England, Scotland and Wales aged 18 and over. [18, 19]  This study was designed as a unique attempt to describe self-reported health, attitudes and beliefs about causes of disease in relation to measurements of health and lifestyle in adults. A total of 12 672 addresses were selected from the electoral rolls of 198 randomly selected constituencies. After excluding empty or demolished dwellings and potential study members in hospital or living in residential accommodation, a total of 9 003 men and women were interviewed. The socio-economic profile of this sample compares favourably with UK census data based on selected variables. [20]  Ethical approvals were obtained from the British Medical Association ethical committee.
[18]  Each study participant gave its written informed consent. The baseline fieldwork consisted of three phases. A face-to-face interview was carried out to collect details on marital status, employment history, smoking habits, alcohol consumption, physical activity and long-standing illness or disability. Data on occupation were used to derive occupational social class in accordance with the scheme of the UK registrar General. Interviewees then had their height, weight and waist circumference measured by a trained nurse. Finally, these participants were requested to complete and mail a self-completion questionnaire assessing personality and psychiatric status, including the 30-item General Health Questionnaire (GHQ-30). [21]  Re-survey (HALS-2) In 1991, seven years after the first survey, a follow-up study (re-survey) was carried out in order to identify changes that occurred in health and lifestyle among the original respondents.
Discussion Findings from this well characterized population-based cohort of British men and women provide evidence that common mental disorders may be a risk factor for future obesity. This relationship was found for measures of obesity based on BMI (men and women) and waist circumference (women only). Similar patterns of associations were found in women when BMI and waist circumference as a continuous, rather than categorical, variables were used as markers of anthropometry. We found no evidence of a bidirectional or reciprocal association in which obesity also predicts future risk of common mental disorder. Despite high response at the successive data collection phases, differential drop out rates between the two surveys due to death, refusal and non-tracing affected the sample distribution.
A global estimation of CO 2 fluxes in the ocean has been derived at 1 • × 1 • spatial resolution from global atmosphere observations used into a data assimilation system for CO 2 called CarbonTracker  (Peters et al. , 2007). In all these datasets the rather coarse spatial resolution leads to uncertainties in the actual estimate of the sources and sinks 100 of CO 2 , calling for an improvement of the resolution of CO 2 flux estimates. In this regard, the last few years have seen the appearance of interesting new developments on multiscale processing techniques for complex signals coming from Earth Observations  (Yahia et al. , 2010). These methods make use of phenomenological descriptions of Fully Developed Turbulence (FDT) in nonlinear physics, motivated by the values taken on by Reynolds number in ocean dynamics.
70  (in 1995 and 1996)  and -2. 02Mt C/year in 2005  (Santana-Casiano et al. , 2009; Monteiro, 2010) , with a strong variability between 2005 and 2006 from -1. 17 to -3. 24 mol C/m 2 per year, respectively  (González-Dávila et al. , 2009). This paper is organized as follows: Sect. 2 describes the datasets used as input in our algorithm. Sect. 3 is devoted to describe the methodology used through the study. Statistical description of the input datasets is presented in Sect. 4. Results of the inference method are given in Sect. 5 by providing outputs of our algorithm, then evaluating the various satellite products and assessing the performance of the method using in situ measurements.
esrl. noaa. gov/gmd/ccgg/globalview/). We use values taken at the closest sea-level station to the Benguela, located at Ascension Island (7. 97 • S and 14. 40 • W) as our reference atmospheric CO 2. The partial pressure of CO 2 is determined from its mole fraction (xCO 2 ), using the following equation  (Dickson et al. , 2007) : pCO 2 = xCO 2 •p, where p is the total pressure of the mixture. We assume this pressure to be close to 1 atm for the conversion following ORNL/CDIAC 105 report (Program developed for CO 2 Systems calculation). The raw data of CarbonTracker fluxes of CO 2 in the area of interest are strongly binned and exhibit strong gradients across those bins. This turns out to be suboptimal for our super-resolution approach. Garbe and Vihharev (2012)  have developed an optimal control approach to invert interfacial fluxes using a simplified inverse problem of at-250 mospheric transport.
The inverse problem is solved using the Galerkin finite element method and the Dual Weighted Residual (DWR) method for goal-oriented mesh optimization. An adaptation of this approach has been applied to the CarbonTracker data set. However, the estimations are expen-255 sive and computing results for all the time frames of interest was infeasible. Therefore, an anisotropic diffusion-based approach has been applied to the raw fluxes of the Carbon-Tracker data set. The diffusion is steered by the direction of the low-altitude wind field. The results thus retain the structure of the CarbonTracker fluxes very well while suppressing artifacts. Examples of this process are shown in Fig. 1. Results are comparable to the physically more accurate approach of  Garbe and Vihharev (2012). 2. 2 Input data: Satellite Ocean data at high resolution 265 Oceanic pCO 2 is a complex signal depending, at any spatial resolution, on sea surface temperature, salinity, chlorophyll concentration, dissolved inorganic carbon, alkalinity and nutrients concentrations.
Both the biological pump, with chlorophyll a as a proxy, and the physical pump, driven by 270 the temperature and salinity (e. g. solubility, water mass), govern the evolution of pCO 2 in the surface ocean. We use here the high resolution satellite ocean data for chlorophyll a, as a proxy for the biological carbon pump and for Sea Surface Temperature (SST), as a proxy for the ther-275 modynamical pump, (see Section 3. 2 for more details on the connection of these oceanic variables).
280 MERIS (MEdium Resolution Imaging Spectrometer Instrument) is on board the ENVISAT satellite and provides daily maps of ocean colour at 1/24 • (∼4 km). Ocean colour from GLOBCOLOUR product is obtained by merging data provided by MODIS (MODerate Resolution Imaging Spectro-285 radiometer), MERIS and SeaWiFS instruments. The Chla concentration is provided daily and at the spatial resolution equal to 1/24 • (∼4 km). Ocean Colour data have been regridded at 1/32 • by linear interpolation. GLOB-COLOUR products are generated using different merg-290 ing methods (see the GLOBCOLOUR Product User Guide document in http : //www. globcolour. inf o/CDR Docs /GlobCOLOU R P U G. pdf ): -Averaging from single-instrument chl-a concentration. In this case CHL1 daily level 3 (L3) products 295 are generated for each instrument using the corresponding L2 data.
Sea Surface Temperature (SST) We use SST derived from OSTIA and MODIS products. OSTIA (Operational SST and Sea Ice Analysis system) is a new analysis of SST that uses satellite data provided by the GHRSST (Group for High Resolution SST) project, together with in situ observations to determine the SST with a global coverage and without missing data. The datasets are produced daily and at spatial resolution of 1/20 • (∼6km) performing a multi-scale optimal interpolation using correlation length scales from 10 km to 100 km (more details in  Donlon et al. (2012) ). The other SST product used in this study is derived from MODIS (MODerate Resolution Imaging Spectroradiometer) sensors carried on board the Aqua satellite since December 2002. This SST product is derived from the MODIS mid-infrared (IR) and thermal IR channels and is available in various spatial and temporal resolutions.
We use Level-3 daily maps of SST at the spatial resolution of 1/24 • (∼4 km)  (Savtchenko et al. , 2004). In Fig. 3 a ) and b), we show one snapshot of SST from OSTIA and MODIS respectively corresponding to the same day on September 21, 2006. In the case of OSTIA products, the SST field is fully covered of points while for MODIS products there are gaps due to cloudiness. On other hand, MODIS product offers a more detailed visualization of the small structures. All SST data have been regridded at 1/32 • by bilinear interpolation.
Preliminary analysis of Sea Surface Temperature (SST) and Chlorophyll images Since the key element for the application of our inferring algorithm relies on the ability to obtain the singularity exponents and their quality, the success of our methodology applied to satellite data depends on the quality and the properties of the input data. In order to assess such properties we perform a statistical analysis of the different datasets. First, we analyze the Chl and SST Probability Distribution Functions (PDFs). In Fig. 4a ) we present the PDFs for Chl from MERIS, GLOBCOLOUR-GSM and GLOBCOLOUR-AVW; the required histograms are built using daily Chl values over 2006 and 2008 at each point of the spatial grid in the area of Benguela. Each one of these PDFs is broad and asymmetric, with a small mode (i.
620 We have repeated the same analysis for SST datasets. The PDFs of the SST values for OSTIA and MODIS products are shown in Fig. 4b ). In this case both PDFs possess similar shape, broad with the mode around 18 • C with a much less deviation from gaussianity as compared to Chl values. This 625 is confirmed with the computation of the statistical moments showed in Table  1. We obtain small values of the standard deviation and kurtosis in both cases, although slightly higher in the case of MODIS. The kurtosis is less than 3, meaning that there is not an important number of atypical values of 630 SST and therefore weak and short tails in the PDFs. If turbulence is dominated by coherent structures localized in space and time, then PDFs are not Gaussian, and the kurtosis will be higher than 3.