dataesr/scientific-paragraphs-categorization huggingface dataset:
D. 2 IMRT Treatment plans were generated using commercial software (Eclipse, Helios, version 8. 2. 23; Varian Medical Systems, Palo Alto, CA). Beam geometry consisted of seven coplanar split fields with gantry angles of 0°, 45°, 110°, 165°, 195°, 250°, and 325°. IMRT was delivered using an 18 MV linear accelerator (21 EX; Varian Medical Systems) and the "sliding window" mode of the multileaf collimator (MLC Millennium 120; Varian). Calculation was performed with AAA algorithm and grid of 2. 5 mm.
D. 3 RA RapidArc plans (Varian Medical Systems) were performed using the Eclipse software version 8. 9. 08 (Helios; Varian Medical Systems). A maximum dose rate of 600 MU/min and 18 MV photon beams were selected. RapidArc with one arc (RA1) corresponded to a single 360° rotation and RapidArc with two arcs (RA2) to two coplanar arcs of 360° sharing the same isocenter and optimized independently and simultaneously. These two arcs were delivered with opposite rotation (clockwise and counterclockwise), so that off-treatment between the two beams was minimized to about 25 seconds. For RA1, field size and collimator rotation were determined by the automatic tool from Eclipse to encompass the PTV. We ensured that the collimator was rotated to a value different from zero in order to avoid the tongue-and-groove effect.
Hydrological data We started our analysis with a hydrology dataset composed of time series of daily mean flow from over 3800 gauging stations distributed throughout France, acquired from the HYDRO database (http://www. hydro. eaufrance. fr/). We removed stations from the dataset that lacked quality-assured data as defined by the HYDRO database managers, and for which the flow records were coded as modified due to the presence of reservoirs, diversions or significant abstractions in the upstream catchment. From the remaining stations, we selected those for which flow data were available in the 35 yr period from 1975 to 2009. We searched these records for gaps longer than 20 days and removed the year of record in which these gaps occurred. This resulted in an average removal of 1. 2 (std dev = 1.
Acknowledgements. Ton Snelder  was supported by  Marie Curie Incoming International Fellowship  within the 6th European Community Framework Programme and by a joint  Irstea-Onema  research project on Low Flow Hydrology). We thank  André Chandesris  for assistance with hydrological data and the  French Water Agency Rhône-Méditerranée-Corse  for financial support. We acknowledge the use of data supplied by the  French database Banque Hydro and Météo-France. Ton Snelder  and  Scott Larned  were funded by the  New Zealand Foundation for Research, Science and Technology, Environmental Flows Programme  ( C01X0308 ). Edited by:  D. Mazvimavi.
Informed consent was obtained from all NHANES participants. For the present study we used publicly available NHANES data from the  1999-2000 and 2001-2002  surveys, in which time to walk 20 ft (at usual walking pace) was measured in participants ≥ 50 years of age. We excluded participants (n = 230) who had had a myocardial infarction in the pre ceding 6 weeks; chest or abdominal surgery within the preceding 3 weeks; a history of brain aneurysm or stroke, knee surgery or knee replacement, or severe back pain; used an assistive device during walking speed measurement; or had Parkinson's disease or reported taking Parkinson's medication. We also excluded four people with improbably high walking speed (> 6 ft/sec). Our final sample for blood lead and walking speed con sisted of 1,798 women and 1,795 men.
2002; Kuo et al. 2006a Kuo et al. , 2007b)) , but also could be mediators of the effects of lead. Finally, we considered scores on the Digit Symbol Substitution Test (DSST)  (Wechsler 1997 ) as a measure of cognitive function, which were available for participants ≥ 60 years of age. Specifically, the DSST is a test of visuospatial and psychomotor speed that is frequently used as a measure of frontal lobe executive function, and has been negatively associated with walking speed  (Kuo et al. 2007a). Statistical analysis. We used SAS, ver sion 9. 2 (SAS Institute Inc. , Cary, NC, USA) for all analyses. We combined NHANES cycles using NHANES cluster design vari ables (SDMVSTRA, SDMVPSU) and medi cal examination weights (WTMEC4YR) for 1999-2002. We used PROC SURVEYREG to per form separate adjusted linear regressions for men and women with walking speed as the dependent variable.
Within the ESA Climate Change Initiative (CCI) project Aerosol cci (2010)(2011)(2012)(2013), algorithms for the production of long-term total column aerosol optical depth (AOD) datasets from European Earth Observation sensors are developed. Starting with eight existing pre-cursor algorithms three analysis steps are conducted to improve and qualify the algorithms: (1) a series of experiments applied to one month of global data to understand several major sensitivities to assumptions needed due to the ill-posed nature of the underlying inversion problem, (2) a round robin exercise of "best" versions of each of these algorithms (defined using the step 1 outcome) applied to four months of global data to identify mature algorithms, and (3) a comprehensive validation exercise applied to one complete year of global data produced by the algorithms selected as mature based on the round robin exercise.
The algorithms tested included four using AATSR, three using MERIS and one using PARASOL. This paper summarizes the first step. Three experiments were conducted to assess the potential impact of major assumptions in the various aerosol retrieval algorithms. In the first experiment a common set of four aerosol components was used to provide all algorithms with the same assumptions. The second experiment introduced an aerosol property climatology, derived from a combination of model and sun photometer observations, as a priori information in the retrievals on the occurrence of the common aerosol components. The third experiment assessed the impact of using a common nadir cloud mask for AATSR and MERIS algorithms in order to characterize the sensitivity to remaining cloud contamination in the retrievals against the baseline dataset versions.
In order to quantify the influence of each assumption, several experiments were then carried out by producing global one-month datasets from eight precursor algorithms with different prescribed aerosol properties and cloud masking. Section 2 summarizes the analysis concept of Aerosol cci. The common steps to improve and harmonize the algorithms are described in Sect. 3. These steps included the definition of common aerosol components and an aerosol-type climatology, and the definition of a common nadir cloud mask. Section 4 describes the algorithms participating in the analysis and the specific implementation of the experiments for each of them. Section 5 gives an overview of the datasets produced, the evaluation tools used and the results of the experiments. The results are discussed in Sect.
The overall concept for the qualification of AOD algorithms in Aerosol cci consists of three steps: (1) several algorithm experiments conducted on a minimal statistically significant amount of data in order to understand the effects of major assumptions (this paper); (2) a round robin exercise (four months, one in each season) to evaluate the improved algorithms versus a more comprehensive independent ground-based dataset and thus identify mature algorithms (de  Leeuw et al. , 2013) ; and (3) the production of a complete validated one-year ECV product for assessment by the climate model community. For the experiments, datasets covering the entire globe for one complete month  (September 2008)  were chosen as a compromise between statistical significance and production effort with eight algorithms and several experiments.
The evaluation of the datasets was conducted by consideration of statistical parameters (mean bias, root mean square error (RMSE), and Pearson correlation coefficient) obtained from comparison of gridded 1 • latitude longitude daily satellite products (level 3) versus AERONET daily averaged aerosol optical depth (AOD) interpolated to a reference wavelength at 0. 55 µm. All experiments began with an analysis of baseline datasets for each algorithm (prior to making any changes). This paper summarizes the algorithm experiments conducted as listed in Table  1  and the analysis made on them: (0) baseline datasets produced with the pre-cursor algorithms prior to any changes; (1) the use of a common set of optical aerosol properties with four components, which were externally mixed; (2) the additional use of an AeroCom/AERONET-based aerosol-type climatology as a priori information; (3) the use of a common AATSR nadir cloud mask for ENVISAT algorithms.
Furthermore, because physical, chemical and optical properties of particles with radii smaller or larger than about 0. 5 µm are usually quite different, the size distributions used in aerosol retrievals are usually described by a bi-modal distribution (n = 2 in Eq. 1). The two size modes are commonly referred to as the fine and coarse modes. For the Aerosol cci experiments, the choices made for r gi and σ i are presented for each size-mode in Table 2  (de Leeuw et al. , 2013). These choices were based on probability distribution statistics derived from AERONET analysis, provided in Fig. 1 , and detailed literature review of the various definitions currently in use in the eight precursor and other aerosol retrieval algorithms. In basing these choices on AERONET statistics, the authors are well aware of existing limitations (bi-modal size distribution, assumptions on refractive indices) but consider this dataset as the most comprehensive and uniform available source of aerosol property knowledge.
The ESA MERIS standard retrieval The MERIS standard aerosol retrieval over land algorithm was originally designed to work over Dense Dark Vegetation (DDV) targets and was extended to brighter surfaces (as DDV spatial cover is low) where the spectral albedo can be predicted as it is linearly related to Atmospherically Resistant Vegetation Index (ARVI). In calculating ARVI, MERIS benefits from the available blue channel which is missing in AATSR. Cloud contamination is the main issue of the standard product as the ESA standard MERIS cloud mask is not robust enough over land (the MERIS instrument has no thermal infrared bands). Assessing the use of the common cloud mask (experiment 3) derived from AATSR APOLLO (together with the 10 km safety zone) was thus of high interest for this MERIS algorithm, although this led to a vast reduction of the data coverage.
The Aerosol Load and Altitude from MERIS over Ocean retrieval for MERIS (ALAMO, ocean only) The MERIS ALAMO (Aerosol Load and Altitude from MERIS over Ocean) algorithm has been primarily developed for aerosol altitude retrievals using MERIS data  (Dubuisson et al. , 2009). Necessary inputs for altitude retrievals, such as aerosol optical properties, are derived in a first step with an initial assumption on the layer altitude. The cloud masking and AOD retrieval schemes are a close adaptation of the MODIS algorithm. The aerosol products of ALAMO include the optical depth and the mixing ratio of fine and coarse modes. Aerosol models used for ALAMO baseline are the same as the ones used for the most current version of MODIS products. In a second step the altitude of the aerosol layer is estimated using the MERIS O 2 A absorption channel.
Evaluation approach For evaluation, 1 • × 1 • gridded level 3 satellite datasets, produced with the Aerosol cci experiments covering the month of September 2008, were compared with daily averaged AERONET sun photometer data (daylight hours only). The daily satellite data (in fact one daytime snapshot per satellite) were retrieved on the days when AERONET observations in the grid were reported. Coherent pairs of valid daily observation from satellite and sun photometer were thus retained at each station for clear-sky conditions when both sun photometer and satellite retrievals were successful. AeroCom tools were used to evaluate the Aerosol cci satellite retrieval versions. These tools were initially programmed to perform analyses for the AeroCom project (http://aerocom. met. no/). They perform comparisons between models and models and between models and groundbased measurements for many model parameters and can read observations from about 20 different observation networks for different variables  (Schulz et al.
, 2009). The outputs are, e. g. maps, difference maps, measurement number maps, time series on the location of ground-based observations, zonal mean plots, model versus observations scatter plots, statistical analyses (e. g. correlation, Bias, RMSE), etc. In order to facilitate the access to the analyses, the output of the AeroCom tools is presented via a web interface (http: //aerocom. met. no/cgi-bin/aerocom/surfobs annualrs. pl). An adaptation of the tools was made to enable them to use gridded daily satellite-based data as a new data source (by treating the satellites as a model) and to account for the specifics of satellites as a data source (e. g. work only with common data points of the different satellite retrievals as described in de  Leeuw et al. , 2013). To quantify the performance of the different versions of each retrieval algorithm in the experiments, reference data sets were compiled from sun photometer data.
High-quality AOD data is provided by the ground-based sun/sky photometer networks of AERONET, PHOTONS, SKYnet and GAW  (Holben et al. , 2001). In contrast to aerosol remote sensing from space, these ground-based transmission measurements require no a priori assumption of aerosol absorption or radiative background. The error in individual retrieved AOD measurements has been estimated  (Eck et al. , 1999; Dubovik et al. , 2002)  to be ∼ 0. 01, or 5-10 % for AOD values smaller than 0. 2. Using AERONET reference data averaged over the day somewhat increased their uncertainties in cases of highly variable aerosol conditions. Even though limited to the land-based observation sites, having access to a global set of sun/sky photometer data provided the possibility to establish solid statistics. In this evaluation total AOD interpolated to 0.
55 µm from the direct sun observations of AERONET, level 2, version 2 were used. This paper focuses on the global statistical analysis (separated for land and coastal sites) and in addition looks into few selected regions representative for different aerosol regimes. The differentiation between land and coastal pixels was made on the basis of the land/sea flag in the ORAC datasets on a 10 km grid. The regions analysed are South America and surrounding oceans (60 • S-20 • N, 105-30 • W representing biomass burning), northern Africa and the Mediterranean (0-45 • N, 20 • W-50 • E, representing mineral dust), and East Asia (0-50 • N, 90-150 • E, representing anthropogenic smoke). Europe and North America, where the bulk of the global AERONET stations are located, are included in the global statistics.
Also the global coverage of the different datasets could be estimated, showing some differences in extending to high latitudes and cloud-induced gaps in the tropics. Figure  6  shows the baseline datasets for all eight algorithms prior to the experiments. Although there is qualitative agreement on several of the characteristic features, there are also many qualitative and quantitative differences between the maps over both land and ocean (e. g. global oceans, biomass burning in South America and Africa, industrial pollution in East China and India, Europe and North America) and large differences in the global mean AOD values ranging from 0. 09 to 0. 32. Figure  7  shows the results of the first experiment using the common aerosol components for seven of the retrievals. There is a general tendency for better agreement of the features for most but not all of the algorithms and a significant reduction of the differences in the global mean AOD values, now ranging from 0.
13 to 0. 22. High AOD regions due to biomass burning in South America and Central/South Africa and over adjacent oceans, or due to North African dust are now at least partly visible in all of them, which could be a result of allowing larger absorption with mid-visible singlescattering albedo of the strongly absorbing component at 0. 8 (e. g. the MERIS algorithms, see Table  3 ). Also over India, East China, Europe and North America the differences between most of the datasets are reduced, although the features still do not agree everywhere. Four algorithms now have similar background oceanic AOD (and the other three agree at least in the tropics). MERIS has very high AOD at high latitudes and for SYNAER the high AOD features in Central Asia of the baseline dataset get even more pronounced.
07 to 0. 26 which differ more than in experiment 1. The use of the common cloud mask in the third experiment is shown for four retrievals in Fig. 9. It is noted that ADV and MERIS-STD used the common climatology (thus comparing experiment 3 directly to experiment 2), while SU and SYNAER went back to free retrieval (thus comparing experiment 3 to experiment 1). The impact of using the common cloud mask is relatively small for the retrievals using AATSR. For SYNAER (where only the size of the safety zone was changed because APOLLO was used in the baseline version already) a reduction of numbers of available dark fields was observed, which led to a minor change of the resulting map. The largest change is visible in the MERIS-STD dataset, when using the AATSR-based common cloud mask with a significant reduction in coverage and a general reduction of AOD values.
Global mean AOD of the algorithms show a similar range as in experiment 2. For the maps shown in Figs. 6-9 the numbers of pixels contributing in each grid box to the monthly mean is shown in the appendix (Figs. A1-A4 ). It is obvious that MERIS and PARASOL provide more pixels (up to 15) than all three AATSR datasets (up to 6), due to its much smaller swath, and even further than SYNAER limited in addition to AATSR by Fig. 6. September 2008 unweighted monthly mean ADO550 for eight total column precursor algorithms: baseline datasets (experiment number 0 in Table  1 ). From top left to bottom right: AATSR ADV, AATSR ORAC, AATSR SU, SYNAER, MERIS BAER, MERIS STANDARD, MERIS ALAMO (ocean only), PARASOL (ocean only). the nadir/limb alternating of SCIAMACHY (up to 3). Large differences of numbers occur for the three MERIS datasets (BAER exploiting by far more pixels, which points to an issue of cloud flagging and ALAMO exploiting only few pixels).
Figures A5 to A12 in the supplementary material show these scatter plots of satellite AOD at 0. 55 µm versus AERONET (all gridded daily means) for the datasets produced by the various algorithms and experiments as shown in Figs. 6 to 9, separated over land and ocean. It is obvious that, except for PARASOL, all datasets showed a clearly weaker performance than the reference datasets (e. g. MODIS, MISR). For each algorithm and each experiment conducted, the results of the analysis depicted in the scatterplots are summarized in Tables 4a-e. These data clearly show that the number Fig. 7. September 2008 unweighted monthly mean AOD550 for seven total column precursor algorithms as in Fig. 6 : datasets with use of common aerosol components and (partly) free retrieval (experiment number 1 in Table  1 ).
of data points coincident with AERONET observations may change significantly between the different algorithms and between the experiments. Similar observations to the numbers of all pixels used in the maps (Figs. A1-A4 ) can be made with the satellite-AERONET data pairs. Evidently, the wider swath of MERIS provides larger numbers of data points compared to AATSR. The synergetic retrieval constrained by two instruments and with even larger pixel size had the lowest number of data points, as expected. PARASOL also has a large pixel size, resulting in fewer data points. But even when the same sensor was used, the numbers differed due to different quality thresholds for cloud-free super pixels of 10 × 10 km 2 within the level 2 products. Outstanding changes in numbers of data pairs between experiments of one algorithm are obvious for MERIS.
For the MERIS standard algorithm, the use of the common cloud mask based on AATSR led to a major decrease as one would expect in view of the reduction to half of the swath width. However, the decrease went much further and yielded even significantly fewer pixels than for AATSR-only algorithms. The reason for this needs further investigation. Interestingly, for ALAMO the coverage almost doubled with using the common aerosol components, while SYNAER had a reduced number of data pairs. This indicates sensitivity in algorithm convergence, depending on aerosol assumptions. In addition to the global analysis for all land and all coastal stations, regional analysis was conducted in three regions each representing a different aerosol regime (South America biomass burning, northern Africa/Mediterranean mineral dust, East Asia anthropogenic smoke).
Interpretation of the results for the different regions needs to account for the different numbers of data pairs in each region and the Fig. 8. September 2008 unweighted monthly mean AOD550 for five total column precursor algorithms as in Fig. 6 : datasets with use of common aerosol components and AeroCom/AERONET climatology as prescription or a priori information (experiment number 2 in Table  1 ). implications for the statistical reliability. Also, this is why the visual assessment of aerosol features and coverage in the monthly mean maps discussed earlier was taken into account. Filtering for common data points over all four experiments and eight algorithms, as applied in the round robin analysis described in de  Leeuw et al. (2013)  for which four months' worth of data was analysed, was not done here, since this would have reduced the number of common points in all 24 datasets to a statistically weak sample.
9. September 2008 unweighted monthly mean AOD550 for four total column precursor algorithms as in Fig. 6 : datasets with use of common aerosol components and (partly) free retrieval or AeroCom/AERONET climatology as a priori information and common cloud mask (experiment number 3 in Table  1 ). or even a small negative effect. For SYNAER the bias was reduced by the common aerosol properties and even further by the increased safety zone in the cloud mask (probably reducing remaining cloud contamination), but the use of the aerosol-type climatology increased the bias, which is probably due to the fact that the surface parameterization used for all experiments has too bright a surface albedo assumptions and thus prefers the wrong aerosol absorption; impact on RMSE and correlations was mostly small or even negative.
Discussion and conclusions Several experiments were conducted in order to understand the role of major modules in eight European aerosol retrieval algorithms. For the experiments, datasets covering the entire globe and one complete month (September 2008) were produced in order to allow for statistical analysis and include cases in all climate zones and with all major types of aerosol. It can be questioned whether a one-month global dataset is sufficient for identifying the impacts of algorithm changes, but this approach was chosen as a pragmatic tradeoff between statistical soundness and processing efforts. The subsequent analysis steps (round robin exercise with four months, one in each season, see de  Leeuw et al. (2013) , and validation with complete twelve months of the same year, in preparation) prove that the limited analysis of only one month of global data summarized here has helped to identify possible improvements (both demonstrated in this paper, such as the revised optical aerosol model, and identified for subsequent algorithm development such as postprocessing to further reduce cloud contamination) and to ultimately reach algorithms which performed significantly better than the baseline algorithms.