dataesr/scientific-paragraphs-categorization huggingface dataset:
This could be explained with the calibration of the BPLUT tables used in the MOD17 algorithm  [9]  using large-scale global terrestrial NPP data  [27]. The calibration data most likely represents average forest conditions and may not capture very open or very dense forests adequately. The NFI NPP on the one hand represents the conditions of the (small) area covered by an inventory plot, while MODIS NPP provides a smoothed average NPP of a 1-km pixel. A consistent stand density map at 1-km resolution would be needed to test this hypothesis. But NFI NPP estimates capture not only differences in stand density and forest management, they are also strongly influenced by local tree allometries and local carbon estimation methods  [38]. For Germany, stand density cannot explain the observed discrepancies satisfactory in Figure  7b.
In fact the results are quite different compared to whole Europe (Figure  6 ), France (Figure  7a ) or our pilot study  [15]. Germany is planning to modify the currently used tree biomass estimation methodology  [39]  which is used in this study, for future carbon assessments. Following reanalysis of existing data  [40]  and collection and analysis of new sample data  [41] , improved biomass functions were developed for Germany  [42]. This new updated methodology results in approx. 5% lower aboveground biomass estimates. Thus future German NFI NPP estimates will be lower as well, which will most likely reduce the gap between MODIS and NFI NPP observed for this country in this study. This suggests that, interpretation of discrepancies between NPP estimates needs consideration of the tree carbon estimation methods, since they directly affect increment estimates.
However, there might be other potential drivers leading to inconsistencies both in MODIS EURO and NFI NPP, that could be analyzed in future studies. Concerning NFI NPP, few countries do not consider adequately the contribution of small trees to the NPP of a forest, either by not considering the ingrowth of small trees  [33]  or a particular large diameter threshold in some countries (Table  S1 ). This could explain, why in Spain and France MODIS EURO is higher than NFI NPP, as we were not able to include ingrowth here and thus the French and the Spanish NFI NPP estimates might not represent the NPP of their forests sufficiently. The accuracy of the litter fall and fine root estimates for NFI NPP (Equations (  3 )-(  5 ) need further research as well. The litter fall models used in this study were derived in a meta-analysis using Eurasian litter fall data  [31].
They have substantial variation in the used input data and might contain potential inaccuracy, when applied in certain regions. In addition, the estimates for litter fall and fine roots are driven by the same climate data than MODIS EURO. Although the specific climate input differs (periodic average climate used in Equations (  4 ) and (  5 ) for NFI NPP versus daily maximum, minimum temperature and precipitation used in MOD17), it cannot be ruled out yet that the climate source explains the better match of MODIS EURO and NFI NPP. Thus, the performance of the currently used approach and alternative options for instance by using Foliage mass and Leaf longevity  [43]  needs to be tested using European litter fall data. Potential errors in the MODIS EURO product could involve wrong classification of forest biomes by MODIS Land cover  [44] , limitations of the global parameters of the MOD17 algorithm capturing European forest conditions (see discrepancies in NPP for evergreen broadleaf forests in Figure  S3 ), mismatches in LAI and FPAR by region or forest fragmentation  [45].
Conclusions In this study we created a regional Net Primary Production (NPP) dataset by running the MOD17 algorithm with local European climate data on 1-km resolution for the years 2000 to 2012 (MODIS EURO). We additionally obtained the global MODIS NPP product (MODIS GLOB) and evaluated the two MODIS NPP datasets with bottom-up forest inventory driven NPP (NFI NPP). We thus compared two conceptually different methods for assessing forest productivity across Europe, and test whether local climate data enhances the ability of the MOD17 algorithm to capture European forest conditions. Running the MOD17 algorithm with local daily climate data substantially improves the quality of MODIS satellite-driven NPP across Europe as compared to the global NPP product (MODIS GLOB). Top-down satellite-driven MODIS EURO and bottom-up NFI NPP agree by regions and by countries, across gradients by longitude, latitude and elevation, if potential discrepancies by stand density due to forest management or the used carbon estimation methods are addressed.
This newly created MODIS EURO dataset is a consistent, continuous, spatial and temporal explicit forest productivity measure of the European forest area providing realistic estimates, which compare well with forest inventory information. This is important since reliable wall-to-wall forest productivity estimates are increasingly important for the growing bio-economy or for increasing our knowledge on other forest ecosystem services such as carbon sequestration. As long as the MODIS program (based on Satellite "Terra" launched in 1999 and "Aqua" in 2002) is operational and local climate data is available, we can obtain reliable large-scale forest productivity measures for European forests. Since the lifetime of the satellites carrying the MODIS sensor is unknown, we strongly suggest the implementation and testing of this concept in the upcoming European satellite technologies such as the Copernicus Programme to ensure consistent and realistic productivity estimates also in the future.
NPP and ∆NPP (always using median) for the whole dataset ("All Countries"), for each country separately and for each region (MODIS NPP using global climate data-MODIS GLOB; MODIS NPP using local European climate data-MODIS EURO and NPP using forest inventory data-NFI NPP); ∆NPP and Rel. ∆NPP both for MODIS GLOB and MODIS EURO. Positive differences indicate that MODIS NPP overestimates NFI NPP and vice versa.
Yet, despite the importance of jobs, the design of policies and interventions to promote opportunities for employment in Sierra Leone has been constrained by a limited knowledge base. Previously, the main source of employment statistics was the 2004 and 2011 Sierra Leone Integrated Household Surveys, which contained limited information on the labor force. To help fill this important knowledge gap, Statistics Sierra Leone, with the support of the World Bank, the ILO, and the Deutsche Gesellschaft für Internationale Zusammenarbeit, designed and implemented the 2014 Sierra Leone Labor Force Survey (SLLFS). This report, relying on the first labor force survey in the country in almost 30 years, provides a picture of the jobs landscape in Sierra Leone. Data from the SLLFS, the first labor force survey in the country since 1984, were collected between July and August 2014 and constitute a nationally representative sample.
This report considers self-employment, decomposed into agricultural and nonagricultural self-employment, and wage and salary employment among possible job types. 7 Unpaid labor is also considered a type of job, although it is not considered employment by the ILO. The sectors of employment are agriculture, fishing, and forestry; mining and extractive industries; manufacturing and utilities; construction; and services. A summary of key employment statistics using these decompositions is presented in table 1. 2. Note: Because of missing information, some individuals could not be assigned to a subpopulation. In these cases, the sum of the subpopulations is less than the overall population, and the statistics on these subpopulations refer to individuals who report the information; individuals on whom data are missing are excluded from the calculations.
Notes 1. The number of observations among the subgroups of youth is small; so, the data should be interpreted with caution. For example, among the subgroup representing tertiary education, there are only six observations. For the certificates for nursing, teaching, agricultural trades, city workers, and trade guilds, there is an average of 23 observations. 2. People who were 37 years old when the sample was surveyed (2014) were 14 years old at the onset of the war in 1991 and were thus too young to be included among the working-age population. Likewise, people aged 17 at the time of the sample were 5 years old in 2002 and are thus not expected to have started school before the conflict had ended. Because people who were of school age during the conflict were 18-37 years old when the sample was taken, it is impossible to assign differences in the distribution of education among this age group relative to the older age group to the effects of the conflict.
Ideally, additional employment should be created in higher-productivity areas, and investment and skills development should aim at more employment and more highly productive jobs. Nonetheless, the largest initial constraint on employment appears to be access to capital; so, policies to improve formal or informal financial markets have the greatest potential to reduce poverty. These policies could target specific groups that are disproportionately likely to stay out of the labor market, such as the population segments that started school, but did not get past secondary education, to maximize the potential reduction in poverty. 64 years, following the definition of the economically active population in the World Bank World Development Indicators database. Unless otherwise specified, for instance, in the case of youth (ages 15-35), it is assumed the age range under analysis is within the working-age population.
As recommended by the ILO, the national system of statistics on employment should cover the labor activities of all age groups, including children on whom separate statistics are needed. Given that the SLLFS collects labor force statistics on individuals five years of age and above, information on children in employment is available in the SLLFS. Child labor, which is the engagement of children in prohibitive work, and, more generally, types of work slated for elimination are measured by the number or share of individuals who, during a specified period, were engaged in work under the worst conditions, employment below minimum age, or hazardous unpaid household services. However, information on working conditions is not collected in the SLLFS. Reference period: In assembling data on current labor activities, all questions relate to a short reference period, the last full week (Monday to Sunday) preceding the date of the interview.
Indicators on Nonfarm Household Enterprises Nonfarm household enterprises, which, in the context of Sierra Leone, are nearly synonymous with the informal urban economy, represent a substantial proportion of all economic actives. Information on traded products and types of business can be obtained from the survey. Indirectly, the data on business owners within the household, any household members involved, and the location of the enterprise operates can also shed light on the types of the businesses in which household enterprises are involved. Finally, business profits can be calculated using revenue and cost information collected through the survey questionnaire.
Key labor market indicators and income data can be compared on youth who have not undergone training and youth who have undertaken technical and vocational education and training or who have served apprenticeships. A similar comparison can be made across youth pursuing training in different fields. Besides constraints in skills and education, youth also face financial constraints, especially self-employed youth in the nonfarm sector. Indicators of access to finance and credit such as information on the sources and amounts of start-up Findings from the 2014 Labor Force Survey in Sierra Leone http://dx. doi. org/10. 1596/978-1-4648-0742-8 capital required, access to credit, business credit, and emergency credit in the previous 12 months are included in the analysis. A few other issues are critical to youth employment.
17 (Medical benefits) are selected. (ii) Formality of self-employment If an individual is in self-employment Corresponding variables in without regular employees Concepts Concept subcomponents SLLFS database (Option 3 or 4 in E. 9, employment Employed (i) Worked in the past week status) then she or he has informal Option 1 in C. 2 (Confirm if respondent had any economic activity during the employment. last completed week). (ii) Temporarily out of work Options 1, 2, or 3 in D. 2 (Main reasons for being absent from job), or option 1 and 2 in D. 3 (Time length being away from work), or option 1 in D. 4 (Receiving pay or returns while not working) Corresponding variables in Unemployment Group of indicators (i) Without employment Indicators "No" option in D. 1 (Have economic the 2014 SLLFS Main sectors and types of employment (ii) Currently available (iii) Seeking employment Employment distribution for primary current activities activities to return to) E.
Acknowledgements This work was supported by the  EPSRC  (grant numbers  EP/ L014416/1 ,  EP/M023885/1  and  EP/J002208/2 ),t he  Nuclear FiRST DTC  and the  Universityo  fM anchester. L. M. thanks the  Humboldt Foundation  and the  Chinese Academy of Sciences  for experienced researcher grantsa sw ell as CalMip for computing time. The authors thank  Prof. F. G. N. Cloke  and  Dr N. Ts  oureas(  University of Sussex ) for helpfuld iscussions, advice and access to equipment formechanistic studies. Keywords: actinides • reduction • small molecule activation • subvalent compounds • thorium.
In this work, we compiled plastid sequence data for 81 accessions of 20 oak taxa of Group Ilex. The main sampling effort was put into the four species currently occurring in the Mediterranean and adjacent regions in North Africa (Atlas Mountains) and northern Turkey (Black Sea region): the widespread Quercus ilex L. and Q. coccifera L. , and the two East Mediterranean narrow endemics Q. aucheri Jaub. & Spach. and Q. alnifolia Poech. Data for additional 56 individuals of ca. 40 species were also produced to integrate all subgeneric Quercus groups and their worldwide geographic distribution. Additionally, Fagales data sets were harvested from gene banks to allow interpretation of the observed divergence in the plastid markers within a larger taxonomic frame. Our objectives were: (1) to assess the extent of plastome diversity in the Euro-Mediterranean focal group; (2) to outline key phylogeographic patterns within Quercus Group Ilex; (3) to establish major evolutionary steps for the differentiation of the 'Old World Clade'.
Plant material, DNA amplification and sequencing Our analysis included 59 individuals of the four Mediterranean Quercus Group Ilex species (File S1) covering their entire range in North Africa and western Eurasia. Additionally, 22 individuals of 16 Asian species of Group Ilex were analysed. The final dataset also included all species of the western North American Group Protobalanus (five species, 10 individuals), 16 species of Group Quercus (20 individuals, from North America and Eurasia), five species of the East Asian Group Cyclobalanopsis (11 individuals), seven species of the American   & Grimm, (2010) ; nomenclature followed  Govaerts & Frodin (1998) ; species investigated in the present study are bolded. Taxonomic remarks and species distributions according to  Govaerts & Frodin (1998).
Species Taxonomic remarks Distribution Group Lobatae (eight individuals), and six species of Group Cerris (seven individuals). The outgroup set was represented by one sample each of the monotypic genera Notholithocarpus and Chrysolepis (western North America) and one species each of Castanea and Castanopsis (NCBI GenBank accessions HQ336406 (complete plastid genome of C. mollissima), JN044213, JF941179, FJ185053). Based on their genetic (plastid) signatures these genera are the closest relatives of Quercus within the Fagaceae  (Manos, Cannon & Oh, 2008). Q. acrodonta Seemen Includes Q. handeliana A. For voucher information and accession numbers see File S1. The molecular analyses included three plastid DNA regions: a part of the rbcL gene, the trnH-psbA intergenic spacer and a portion of the trnK/mat K region (3 intron and partial gene).
These markers were chosen based on the variability displayed in previous analyses (e. g. ,  Manos, Zhou & Cannon, 2001; Okaura et al. , 2007; Simeone et al. , 2013)  and the high number of their sequences available on GenBank. Primer sequences for the three regions were obtained from  Kress & Erickson (2007) ,  Shaw et al. (2005)  and  Piredda et al. (2011) , respectively. DNA extractions and PCR protocols were the same as in  Piredda et al. (2011). Sequencing of both DNA strands was performedat Macrogen (http://www. macrogen. com), using the forward and reverse PCR primers,; electropherograms were edited with CHROMAS 2. 3 (http://www. technelysium. com. au) and checked visually.
Phylogenetic analyses Multiple sequence alignments for the focal group were obtained with ClustalW 1. 81  (Thompson, Higgins & Gibson, 1994)  and checked by eye. The matrices were concatenated with the Python programme combinex2_0. py (Python v. 2. 6. 4; Biopython 1. 57). Maximum likelihood trees were inferred with GARLI  (Zwickl, 2006;  run on the CIPRES portal, http://www. phylo. org/sub_sections/portal/) using four data partitions (rbcL and mat K codons, trnK intron and trnH-psbA spacer). MrModeltest 2. 0  (Nylander, 2004)  and the Akaike Information Criterion (AIC;  Akaike, 1974)  were used to decide on the best-fitting substitution model for each partition. MrModeltest 2. 0 results were also used for setting up Bayesian inference, performed with MrBayes 3. 4b4  (Ronquist & Huelsenbeck, 2003; Ronquist et al.
, 2012). RAxML v. 7. 0. 4 (Stamatakis, Hoover & Rougemont, 2008)  was used for calculating maximum likelihood bootstrap support (1,000 replicates). Trees were edited with Figtree 1. 3. 1  (Rambaut, 2014)  and Mesquite v. 2. 75  (Maddison & Maddison, 2011). Median-joining (MJ) haplotype networks were inferred with Network 4. 6. 1. 1 (http://www. fluxus-engineering. com/) for each gene region (rbcL, trnK/mat K, trnH-psbA), treating gaps either as missing or 5th state. The MJ algorithm was invoked with default parameters (equal weight of transversion/transition), in order to handle large datasets and multistate characters. Primary data, analyses, results and Files S1-S3 are provided as online supporting archives at www. palaeogrimm. org/data/Smn15_OSA. zip.
Levels of intra-and interspecies plastome divergence in Quercus The entire dataset included 423 plastid DNA sequences (141 samples, three markers each). The sequence quality was high for all the marker regions: 100% of unambiguous full-length electropherograms with 100% overlap between complementary sequences were recovered across all taxa. Table  2  shows that trnH-psbA was the most variable marker region (a 34-bp inversion occurring in approximately 50% of the samples was replaced with its reverse-complementary sequence and a binary character was inserted to keep record of it). As expected, the least variable region was rbcL. No indels were found in the rbcL and mat K coding regions. The combined cpDNA dataset (trnH-psbA, trnK/mat K, rbcL) resulted in an alignment of 2082 characters (sites), of which 122 were variable (thereof 72 parsimony-informative; gaps not considered).
ACKNOWLEDGEMENTS We thank all the  Directors and curators of the Botanic Gardens and Arboreta  who provided the investigated material:  Anthony S. Aiello ,  Wolfgang Bopp ,  Anne Boscawen ,  Peter Brownless ,  Béatrice Chassé ,  Laszlo Csiba ,  Dirk De Meyere ,  Holly Forbes ,  Anett Krämer ,  Isabel Larridon ,  Gitte Petersen ,  David Scherberich ,  Patrick Thompson ,  Jef Van Meulder ,  Michael Wall ,  David Zuckerman , and all the colleagues and friends who contributed to the sampling design:  Jeannine Cavender-Bares ,  Hanno Schaefer ,  Charalambos Neophytou ,  Martina Temunovic ,  Gianni Bedini ,  Laura Genco ,  Enara Otaegi Veslin.
The largest contamination of the dust signal by leakage is about 3. 5 % at = 50, for both the D EE and D BB spectra. Because we consider that our estimate of the leakage maps is conservative, we conclude that contamination of the dust D EE and D BB power spectra by systematic effects amounts to a maximum of 4 % at = 50, and is less at higher multipoles. Therefore, we have not corrected the Planck data for intensityto-polarization leakage in this work. Finally, in Fig. 2  we present for the three LR regions the absolute value of the null-test spectra anticipated in Sect. 2. 3, here computed from the cross-spectra of the HalfRing/DetSet differences, i. e. , (353 DS1,HR1 -353 DS1,HR2 )/2 × (353 DS2,HR1 -353 DS2,HR2 )/2. These D EE and D BB spectra show a behaviour that is close to what is expected from a white-noise dominated (thus 2 ) spectrum.
59 and T d = 19. 6 K (Planck Collaboration Int. XXII 2014). Note that no fit was performed at this stage and that the square of the adopted dust SED goes through the 353 GHz data point. However, if we fit the amplitude of the dust frequency dependence, with fixed β d and T d , the χ 2 (N dof = 9) is 13. 1 for EE (PTE = 0. 16) and 3. 2 for BB (PTE = 0. 96). This good agreement supports the assumption made in the present work about the faintness of the synchrotron and CO emission at high Galactic latitude (see Sect. 2. 2). The residuals to the fit do not show any evidence for an excess power at 100 GHz, which might arise from polarized synchrotron emission. This result is consistent with the study of synchrotron polarization at high Galactic latitudes by  Fuskeland et al. (2014) , and confirmed in Appendix D.
Statistical study of the dust power spectra at high Galactic latitudes In Sect. 4, statistical properties of dust polarization were derived from angular power spectra computed with the Planck 353 GHz data on large fractions of the sky ( f eff sky from 24 % to 72 %). Most of the CMB experiments target fields at high Galactic latitude with a smaller size than this. So now we evaluate the dust Bmode power in such patches. We perform a statistical analysis computing the D EE and D BB power spectra of the 353 GHz Planck polarization data for high Galactic latitude circular patches of size 400 deg 2 , similar to the size of the fields observed in ground-based and balloonborne CMB polarization experiments (see Sect. 3. 3. 2). We verify that the empirical scaling law of the power spectra amplitudes as a function of the mean intensity, derived in Sect.
BB angular power spectrum of dust in the BICEP2 field In this section, we use the Planck data and the results presented above to assess the dust polarization in the field observed by the BICEP2 experiment (BICEP2 Collaboration 2014a,b). As above, finding D BB at 150 GHz involves two steps, measuring the power spectrum at 353 GHz and then extrapolating the amplitude to 150 GHz.
5 σ in this broad bin. In order to assess the potential contribution from systematics, we have computed the dust D BB spectrum on M B2 on different subsets of the data and performed null tests, which are presented in Appendix D. 3. In this lowest bin of , we do not observe any departure from what is allowed by noise. Nevertheless, we stress that below the noise level our cross-spectra could be subject to a positive or negative bias due to systematic effects. For example, if instead of taking the DetSets cross-spectra (as we have done throughout this paper) we take the mean value computed from the DetSets, HalfRings, and Years cross-spectra (presented in Appendix D. 3), the statistical significance of our measurement is decreased from 4. 5 σ to 3. 6 σ. The uncertainty coming from the M B2 definition (presented in Appendix D.