dataesr/scientific-paragraphs-categorization huggingface dataset:
In Situ Observation of the Incorporation of Copolymer Worms in Calcite The mechanisms by which the copolymer worms become incorporated within the growing calcite crystals were studied using in situ atomic force microscopy (AFM). As the worms are signifi cantly larger than the 3. 1 nm step edges on calcite, it was challenging to simultaneously image both the step edges and the worms. The worms were fi rst imaged on the surface of mica (rendered cationic via electrostatic adsorption of poly( Llysine)) in order to characterize their morphologies and the data were in good agreement with TEM images of dried worms (Figure  SI5 , Supporting Information). Growth experiments were then performed by imaging the changes that occur on the exposed {104} surface of a freshly cleaved single crystal of geological calcite when it is exposed to the reaction solution within a commercial fl uid cell.
Acknowledgements This project was supported by a grant from the  US National Science Foundation  ( Materials Word Network Award   DMR 1210304 ,), an  Engineering and Physical Sciences Research Council  ( EPSRC) Leadership Fellowship  (F. C. M. and YYK,  EP/H005374/1 ), and  EPSRC  grants  EP/J018589/1  ( Materials World Network , F. C. M. and Y. Y. K. ) and  EP/K006304/1  (F. C. M. and A. N. K. ). Work at the Molecular Foundry was supported by the  Offi ce of Science, Offi ce of Basic Energy Sciences , of the  U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. The work was further supported by  Hysitron Inc , (Edina, MN, USA). The authors acknowledge  Diamond Light Source  for time on beamline I11 under commissioning time and proposal EE10137. The authors would also like to thank  Prof.
Acknowledgements This work was supported by the  Marie Curie Intra -European Project  FP7-PEOPLE-2013-740 IEF-62647. The authors are grateful to  A. Djemat  from  Animalerie Buffon  for animal care. L. M. L. -M. acknowledges funding from the  European Research Council  ( ERC Advanced Grant  #  267867  , Plasmaquo) and the  Spanish Ministerio de Economía y Competitividad  ( MAT2013-46101-R ).
HBS do not include meals eaten outside the home but does include kitchen wastes and leftovers. IDS refer to edible part (excluding wastes and leftovers) of food and include eating at home and outside of home (47). Finally FBS overestimated food consumption and nutrient intake compared to IDS. Results between HBS and IDS are quite similar, except for fish, meat, pulses, and vegetables, which are underestimated by HBS, and sugar, honey, and cereals, which are overestimated. The worldwide data FBS  1  and the European data HBS 2  are current statistics. An overall view of HBS with the aim of harmonizing data codes for nutritional analysis can be found in the publication related to the DAFNE project  3   (48) , and the results are also published in the "European Nutrition and Health Report 2009" 4 (49).
Data Sources FAOSTAT FBS and commodity balances provide data for domestic availability of a food, and food component in the case of protein  (50). The contributing data include the sum of production and imports, with exports and non-food use subtracted. New modules to the FAOSTAT family of databases, including land use, emission, pesticide, fertilizer, and irrigation, will provide more data on environmental sustainability when analyzed with protein ratio data. Food consumption studies, National Nutrition Surveys, HBS, etc. , will be available in some countries to augment or replace FAOSTAT data.
Limitations of the Indicator FAOSTAT data from FBS and commodity balances reflect domestic availability of foods, not consumption or production per se. While these data have proven useful for assessing nutritional adequacy of diets, with a long history of use, they may significantly misrepresent sustainability issues. For example, livestock production has a greater role in GHG emission than livestock consumption. If meat is imported rather than domestically produced, the calculation of environmental impact may be skewed if using food balance or commodity balance datasets. Similarly, National Nutrition Surveys do not address the issue of production. Food losses and waste not accounted for in the datasets will affect the calculations and interpretation. Additionally, the advantages of using plant:animal protein ratio, as opposed to plant:animal dietary energy ratio or plant:animal ratio in grams per person per day, need to be elaborated  (51, 52).
Data on DES can be gathered from FBS and HBS. FBS and HBS overestimate energy intake, although not in a linear way; while FBS includes eating out, HBS does not; food losses and waste should be considered. In order to reduce the impact of possible errors in estimated DES, due to the difficulties in properly accounting of stock variations in major food, the indicator is calculated as an average over 3 years (for example, 2010-2012, 2011-2013, and 2012-2014). Average dietary energy requirement is a reference for adequate nutrition in the population. The recommended level of dietary energy intake for a population group is the mean energy requirement of the healthy, well-nourished individuals who constitute that group. The estimates of requirements derived from measurements of a collection of individuals of the same age, gender, body size, presumed body composition, and physical activity are grouped to give the average energy requirements for a class of people or a population group.
Methodology Parameter considered is as follows: -dietary energy density (kilocalories/gram) calculated by dividing total dietary energy by the edible weight of foods and caloric beverages consumed. The primary data are as follows: • the mean amounts of various foods/beverages or food groups consumed daily. • the energy provided by weight unit of the foods/beverages or food groups as provided by food composition databases. It will be expressed as the amount of energy (kilocalories or kilojoules) in 100 g or 1 k of daily diet.
Limitations of the Indicator Individual Dietary Surveys provide the most accurate figures for actual daily food consumption. Data obtained from FBS do not reflect the effective food intake, because they relate to the food quantities theoretically available for consumption; the amount of food consumed is lower than those reported in FBS, due to the degree of losses of edible food and nutrients in the household, e. g. , during storage, in preparation, and cooking, as plate waste or quantities fed to domestic animals and pets, or thrown away. Depending on data sources and studies, the level of accuracy and units used can vary. Also, the data obtained even from National Dietary Surveys do not reflect the portion size. Indeed, there is evidence that larger portion size of energy-dense and nutrient-poor foods is involved in the increase of overweight and obesity that accompany the changes in dietary patterns in children and adults  (57, 58).
Methodology Parameters considered are as follows: -a daily diet: • mean adequacy ratio (MAR) based on the mean percentage of the recommended intakes for 29 key needed nutrients, alone or in combination with the mean excess ratio (MER) for nutrients to limit. -general purpose: • the amount of every nutrient present in a unit of a given food/beverage or food group as provided by food composition databases; • Nutrient Density Scores referring to either 100 g, 100 kcal/kJ, or cost/kg or L of a given food: ex simplified SAIN/LIM scoring (Score d' Adéquation Individuel aux Recommandations Nutritionnelles -SAIN; nutrients to be limited -LIM)  (10). Some publications cited in the reference list provide examples of calculations and interpretations  (12, 59 ).
Limitations of the Indicator All scores designed to evaluate the nutrient density of either individual foods or whole diet have advantages and limitations. They must be taken into account depending on the precise context and objective considered. The limitations are (i) the need for accurate and quantitative dietary intake data and food composition databases; (ii) comparisons between countries are limited by possibly different daily recommended intakes (energy, nutrients, and fiber); and (iii) comparisons between studies need the use of the same nutrients and total number of nutrients. It has to be considered that the MAR normally should be 29, but because of the lack of composition tables, the number is usually less. In France, for example, 23 includes the different lipids.
Limitations of the Indicator Data obtained from FBS do not reflect the effective food intake, because they relate to the food quantities available to the consumer (but not necessarily consumed). Thus, the amount of food consumed is usually lower than those reported in FBS  (59) , due to the degree of losses of edible food and nutrients in the household/ catering, e. g. , during storage, in preparation, and cooking, as plate waste or quantities fed to domestic animals and pets, or thrown away. However, when National IDS are not available, the HBS and/or FBS provide good indicators by which to compare several countries and different time periods. Although it is not specified in official documents, considering the high proportion of waste often present in preparations of plant foods, it should be specified that the weight of 400 g daily refers to edible product net of waste  (12, 60).
Methodology Parameters considered are as follows: -food composition: a count of the number of foods: • at variety/cultivar/breed level for common foods with at least one value for component found in published and unpublished sources. • at species level for wild/indigenous/underutilized foods with at least one value for component found in published and unpublished sources. -food Consumption: • the taxonomic diversity of foods, as for food composition, reported in food consumption/dietary intake surveys. Data collected and reported include: ◾ the study instrument (e. g. , diet history, food frequency) with details (scope, date, number, and description of subjects, geographical/ethnic coverage; reference, total number of studies examined); ◾ the qualifying biodiverse foods reported (number of foods, food lists); • the number of surveys with at least one reported food counting for biodiversity.
Data Sources Food and Agriculture Organization of the United Nations/ International Network of Food Data Systems (INFOODS) compile data and report periodically  (71). For food composition, data are obtained by searching peer-reviewed journals using the search engines Scopus and Science Direct, and through a call for data conducted via INFOODS. These data are then compiled in a Biodiversity Food Composition Database  (72). For food consumption, data are obtained from all surveys, including National Nutrition Surveys, market surveys, ethnobiological investigations, and inventory studies. All published and unpublished available resources are searched, including peer-reviewed journals, official international/regional/national/ subnational survey reports, conference presentations, and published matter, including posters, abstracts published from meetings, and theses.
Data Sources In most industrialized countries, data on the organic market volume as well as the market shares are available as well as recorded  (73). Detailed data for specific food types can be available too. During some consumer cohort surveys or in national consumption surveys, individual data are collected on organic food consumption (e. g. , Germany, France). Furthermore, national yearly data are now available and continuously recorded  (73)  regarding the importance of organic food production (number of farms, acreage, volume of foods produced) and share of total. In some countries, agricultural production data (at local/regional/national) are also available along with organic food import/export data  (73).
In this paper, we refer to databases: FAOSTAT, HBS, EUROSTAT, etc. The quality of the data can be highly variable, and this may represent a limit of the procedure. Anyhow, there is no means to avoid these difficulties, and the presence of different databases may minimize, at least partly, the effects of the deficiencies of each single database. Thus, the next phase of this work will be: -Collecting data sets from individual countries to ascertain what figures are available for analysis. -The validation of the indicators that were selected by the expert group. Thus these indicators will be performed versus sustainability or versus an outcome variable that can be considered a proxy of it. -The definition of a mathematical model that will be able to combine all the indicators belonging to a single area (e.
The present study concerns the period 2009-September 2012, date of the implementation of a new OMI processing tool. The new version (v1. 3) introduces a correction for absorbing aerosols that were not considered in the old version (v1. 2). Both versions of the OMI UVI products were available before September 2012 and are used to assess the improvement of the new processing tool. On average, estimates from satellite instruments always overestimate surface UVI at solar noon. Under cloudless conditions, the satellite-derived estimates of UVI compare satisfactorily with ground-based data: the median relative bias is less than 8 % at VDA and 4 % at SDR for both OMI v1. 3 and GOME-2, and about 6 % for OMI v1. 3 and 2 % for GOME-2 at OHP. The correlation between satellite-based and ground-based data is better at VDA and OHP (about 0.
99) than at SDR (0. 96) for both space-borne instruments. For all sky conditions, the median relative biases are much larger, with large dispersion for both instruments at all sites (VDA: about 12 %; OHP: 9 %; SDR: 11 %). Correlation between satellite-based and ground-based data is still better at VDA and OHP (about 0. 95) than at SDR (about 0. 73) for both satellite instruments. These results are explained considering the time of overpass of the two satellites, which is far from solar noon, preventing a good estimation of the cloud cover necessary for a good modelling of the UVI. Site topography and environment are shown to have a non-significant influence. At VDA and OHP, OMI 1 Introduction Monitoring of UV solar radiation at the surface is a necessary and important task to characterize the impact of atmospheric composition change, which is the goal, for example, of the Network for the Detection of Atmospheric Composition Change (NDACC) and of the Global Atmosphere Watch Programme (GAW).
Some of these data are products of the instrument itself (ozone, cloudiness) while others come from climatologies  (aerosol content, albedo). Differences between the data of the two satellite instruments that will be used in this work (OMI, the Ozone Monitoring Instrument, and GOME-2, the second Global Ozone Monitoring Experiment) are detailed below. Despite their extensive geographical coverage, satellitebased (SB) data products are affected by measurement uncertainties, as are ground-based (GB) products. However, SB data are also affected by modelling uncertainties. Moreover, due to their rather coarse spatial resolution, SB data sometimes do not capture fine-scale phenomena. Overall, various sites are useful for assessing the satellite data products in various conditions, including various latitudes, land covers, altitudes and climates.
However, validation exercises are difficult to achieve due to differences in temporal and spatial resolutions of GB and SB data products. Extensive comparison studies between surface UV provided by OMI and GB measurements have been previously made  (Tanskanen et al. , 2007; Buchard et al. , 2008; Ialongo et al. , 2008; Weihs et al. , 2008). Those studies dealt with version 1. 2, which did not account for the influence of absorbing aerosols, implying a positive bias in OMI product. The OMI product has been tentatively corrected by several methods  (Kazadzis et al. , 2009a; Arola et al. , 2009; Buntoung and Webb, 2010; Antón et al. , 2012). From the comparisons against GB measurements, the OMI surface UV index (UVI) at sites with low amounts of absorbing aerosols has been shown to be an overestimation of 0-10 %.
Alternatively, at sites with significant influence from absorbing aerosols, OMI surface UVI show a larger positive bias of up to 50 %. All these OMI validations, apart from  Buntoung and Webb (2010) , were conducted using data collected at the time of the satellite overpass. Currently, only one validation study is available for GOME-2, but it only concerns daily doses  (Kalakoski, 2009). For both satellite instruments, the previous validations address data up to 2008, except  Antón et al. (2012)  for OMI. Muyimbwa et al. (2015)  and  Bernhard et al. (2015)  address more recent OMI data. In the present study, validations are conducted using data at noon, when the UVI is maximum for cloud-free conditions, over a more recent period at three French sites, including a new southern site. The Saint-Denis site on Réunion Island is characterized by the proximity of the ocean, a complex topography and a frequent occurrence of orographic clouds forming at around midday.
OMI and GOME-2 websites make available UVI data and maps at solar noon, when values are generally close to the maximum and more risky for health; therefore, comparison with ground-based UVI is carried out in this study at noontime. Validations of satellite-based estimates with ground-based measurements are conducted under cloudless and all sky conditions for about 4 years (January 2009-September 2012), until the date of the implementation of a new OMI processing tool. The new version (v1. 3) introduces a correction for absorbing aerosols that were not considered in the old version (v1. 2). The whole archive has been reprocessed with OMI v1. 3, so both versions of the OMI UVI products are available before September 2012 and are used in this work to assess the effect of the absorbing aerosol correction.
Data processing The GOME-2 algorithm proceeds similarly to OMI algorithm, with slight differences. Surface UV irradiance is estimated via a radiative transfer model using total ozone column, derived from GOME-2 measurements via another dedicated algorithm; surface albedo from the same climatology as the OMI algorithm; an extraterrestrial solar spectrum; and climatological profiles of ozone, temperature, aerosols and clouds  (Kujanpää and Kalakoski, 2015). Aerosol properties come from the Global Aerosol Data Set (GADS)  (Köpke et al. , 1997)  and aerosol optical thickness comes from the cli-matology of  Kinne (2007). Instantaneous cloud optical depth is derived via interpolation of COD retrieved from measurements of AVHRR-3/Metop-A (which is on the same platform as GOME-2, having a morning orbit and the same local overpass time) and AVHRR-3 aboard NOAA satellites on the afternoon orbit (NOAA-18 until 3 June 2009 and then on NOAA-19).
Depending on the station latitude, two or more AVHRR overpasses occur, making two or more COD values available. All input data are mapped to a regular 0. 5 • × 0. 5 • latitude-longitude grid. UVI is derived from spectral irradiance and given on the same grid. For the current study, O3M SAF offline surface UV (OUV) products were reprocessed using the algorithm version 1. 13 with a special option to store diurnal COD values, which are not included in the standard product. Uncertainty in GOME-2-derived UVI is due to uncertainty in the irradiance modelling (depending on ozone, surface albedo, cloud and aerosols). The resulting uncertainty is about 8 % (16 %) in clear sky conditions and about 10-20 % (20-40 %) in cloudy conditions, depending on the number of COD values available. As for OMI, the largest contribution to the uncertainty comes from the cloudiness estimate because UVI is given at noon rather than at the satellite overpass time.
Results Due to their limited spatial resolution, space-borne measurements represent an average value for the observed pixel. Thus, when the cloud cover is not homogenous in the pixel, satellite data should not be directly compared to instantaneous ground-based measurements. For comparison at overpass time, the effect of the cloud variability within a satellite sensor pixel can be accounted for by averaging GB measurements over a time interval around the time of overpass. Here, comparisons are conducted at noontime, and the cloudiness measurements used in OMI and GOME-2 algorithms are not actual values at noontime. Nevertheless, for all sky conditions (AS), GB UVI measurements have been averaged over a time interval around noontime. Several time intervals have been tested and the hourly average of GB values has been selected as a better representative of spatial measurements for both space-borne instruments.
Though the GOME-2 pixel is larger than the OMI pixel, a mean over a larger time interval is not valuable since it would introduce a low bias in the GB product at solar noon (indeed, UVI is generally maximum at noon). For cloudless conditions (CS), to avoid introducing a low bias in the GB product at solar noon (see above), no average was calculated. The selection of CS measurements at noontime cannot be made via cloud information available in the OMI data files since the COD corresponds to overpass time, and for GOME-2, cloud information is interpolated at noon from AVHRR data (see Sect. 2. 2. 2); therefore, the COD value may not really be valid. Thus, CS selection is based on the examination of the GB UVI measurements. Two criteria are set up to declare the sky as cloudless: (i) the shape of the curve of the UVI diurnal variations around noon must be smooth (visual inspection), and (ii) the UVI relative dispersion around the hourly mean must be less than 5 %, with this value being an estimate of the UVI variation due to SZA variation around noontime (estimation derived from modelling).
This second criterion is checked automatically. In addition, images from the SEVIRI sensor on the MSG satellite must show cloud-free conditions close to the measurement time. This method is not perfect because a nearly constant thin cloud cover can be mistaken for cloud-free conditions. We have considered two limits (100 and 10 km) for the distance between the GB station and the cross-track position (CTP) for OMI and the grid cell centre point for GOME-2. Satellite-based and ground-based data sets are compared by computing the UVI difference (SB-GB), the UVI relative difference (SB-GB)/GB) expressed in percent, and by plotting correlation diagrams of UVI. The following statistics parameters are used to quantify the agreement: mean and root mean square of the difference, mean, root mean square and standard deviation of the relative difference.
VDA At this northern midlatitude site, OMI overpasses occur from 0. 5 h before to 2. 5 h after solar noon. The GOME-2 overpasses take place in the morning from 3 to 0. 5 h before solar noon. The VDA site, located in a topographically flat region, is characterized by rather high total ozone columns (on average in the 250-450 DU range) and by the presence of absorbing aerosols of pollutant origin. The surface albedo at 360 nm, provided in the OMUVB database, exhibits a weak seasonality in the 0. 03-0. 07 range. For both satellite instruments, the distance between the ground station and the CTP/grid cell centre point is first chosen smaller than or equal to 100 km. Comparison results for AS conditions are shown in Fig. 1  for both satellite instruments: the upper panels present OMI v1. 3 and the lower panels GOME-2.