dataesr/scientific-paragraphs-categorization huggingface dataset:
With these findings and by updating the enhancement ratios, an interesting modeling work could be performed to estimate a 355 new tropospheric budget for HCOOH. This IASI data set may also be used in the future to study a single plume at different time. This would be useful for the characterization of the chemistry ongoing in a fire plume outflow. An inter-comparison with other space-borne instruments as TES and ACE-FTS will be helpful to interpret the difference and the biases between the retrieved HCOOH columns in thus between their respective ER (HCOOH/CO).
Data availability 360 The IASI FORLI CO products are publicly available via the Aeris data infrastructure (http://www. aeris-data. fr/) while the IASI HCOOH columns will be made available soon through the same platform. Tab. 1 Correlation coefficients between the HCOOH total columns and the CO total columns measured by IASI for the period between 2008 and 2014 over the 7 studied regions. Each IASI data is selected in an area of 50 km around the MODIS fire hotspot and up to 5h after the time recorded for each fire. The number of fires characterized by HCOOH and CO total columns is given in parenthesis. 2. 6×10 -3 ± 0. 3×10 -3  (Chaliyakunnel et al. , 2016)  4. 6×10 -3 ± 0. 3×10 -3  (Vigouroux et al. , 2012)  5. 1×10 -3  (Coheur et al. , 2007)  11. 3×10 -3 ± 7. 6×10 -3  (Rinsland et al. , 2006) * 5.
Acknowledgments The IASI mission is a joint mission of  EUMETSAT  and the  Centre National d'Etudes Spatiales (CNES, France). The IASI L1 data are distributed in near real time by  EUMETSAT  through the EUMETCast system distribution. We thank the  MODIS team  for providing public access to fire products MCD14ML. This MODIS data set was provided by the  University of Maryland  and  NASA   FIRMS  operated by  NASA/GSFC/ESDIS  with funding provided by  NASA/HQ. The authors thank  S. 370 Whitburn  ( ULB ) for his help on the MODIS files. They also thank  J. Hadji-Lazaro  (LATMOS) and  L. Clarisse  ( ULB ) for preparing the IASI ΔT b data set. The authors also acknowledge ECMWF for the free access to the meteorological data.
, 2011). Beyond data from meteorological stations, indices of atmospheric mass distribution can help to provide integrated predictors for broad scale climate in terrestrial ecosystems. The Northern Atlantic Oscillation (NAO) refers to the atmospheric mass distribution between the Arctic and the subtropical Atlantic and has a strong impact on the European climate, including that of the Iberian Peninsula (where low NAO leads to a humid growing season;  Martín-Vide & Fernández-Belmonte, 2001) , as well as that of the eastern coast of North America. NAO can be assessed through an index based on the pressure differences between a northern location (usually Iceland) and more southern locations (usually the Azores Islands or the Portuguese coast). This type of general climate index provides an integrated measure of seasonal weather and offers potentially better description of coarse-scale regional climatic variability than finer-scale averages of specific local variables  (Hurrell & Deser, 2009).
Study area The data were collected from an experimental plot situated in a natural maritime pine (P. pinaster) forest located at 755-810 masl in a flat sandy region in the Castilian Plateau, central Spain (latitude 41º16'N and longitude 4º29'W). Rotation age has been approximately 100 years under a shelterwood system adapted to resin production. In this system, the regeneration period varies from 20 to 25 years in length. Climate is dry Mediterranean, with an annual rainfall of 401 mm and mean annual temperature of 11. 2 ºC. Vegetation is dominated by Corynephorus canescens (L. ) P. Beauv. , Stipa spp. , Retama sphaerocarpa (L. ) Boiss. , Lavandula stoechas L. , Thymus mastichina L. , and isolated trees of Pinus pinea L. A detailed description of the site was given by  Nanos et al. (2004).
Tree data In 2000, a circular plot (radius=100 m) was installed in a mature stand within the study area where only dead trees had previously been removed during thinning operations. On all mature trees (380) the following data were collected: spatial position (polar coordinates, to nearest 0. 1 m), ring count age, total height (to nearest 0. 1 m), number of cones and diameter at breast height (DBH) to nearest 0. 1 cm. Coordinates were measured with a total station. In 2006 and 2007, additional cone counts were conducted on the same trees. Number of cones was estimated on each tree by visual count (see details in  Miguel et al. , 2002). For each tree, BAL, i. e. , the basal area in trees larger than the subject tree  (Wykoff, 1990) , was computed as an index of competition from above and basal area of neighboring trees (BA 6 ) was calculated as an index of two-sided competition.
Both BAL and BA 6 for each subject tree were computed by considering only the six nearest trees. Crown dimensions of each tree were estimated with  Lizarralde's (2008)  crown equations (see Fig. S1  [suppl] for details). Tree dimensions (stem and crown) were assumed to have experienced no change over the course of the study (from 2000 to 2007) because the stand studied is mature and well over its maximum growth period. In 2011, 113 trees were selected by expert judgement to cover the range in cone yield and were cored to obtain radial increments. DBH for these 113 trees ranged from 25. 3 cm to 62. 7 cm while cone counts ranged from 0 to 78 (see more details in Table  1   reanalysis. html). The correlation between a monthly time-series constructed with the Ponta Delgada observational data and one constructed with NCEP/ NCAR reanalysis data was, according to NCAR information, 0.
Determining correct variance-covariance structure Because the data included three non-consecutive measurements from each tree, potential autocorrelation within trees could lead to biased estimates of variances and incorrect p-values for statistical tests. Four August 2017 • Volume 26 • Issue 2 • e07S different variance-covariance structures, typically used in ecological studies  (Barnett et al. , 2010) , were tested in a model for cone occurrence (binomial regression) and in a model for cone intensity (Poisson regression): independent, exchangeable, unstructured and autoregressive of order one. The models with alternative variance-covariance structures were fitted by SAS Proc GENMOD and compared by the quasi-likelihood information criterion (QIC) proposed by  Pan (2001). No evidence could be found for significant autocorrelation in the pooled data, so further data analysis was pursued assuming independence of observations.
This model is more parsimonious that other previously reported for other Mediterranean pines. Calama et al. (2011) , for example, used 41 different variables to estimate Pinus pinea L. cone production. However,  Calama et al. (2011)  used a much larger dataset with ten years of cone harvesting. Longer cone harvest period confers an important advantage because cone set varies dramatically across years. However, differences in reproductive effect measured by cone harvesting records and visual cone counts should also be considered. Different numerical simulations have been conducted to explore the impact of the predictor variables (winter weather during the bud formation and flowering years, tree size, stand density, competition from above, and growth efficiency) on cone occurrence and intensity of cone yield.
FUNDING  CNRS  [ UPR 9073 ];  Université Paris VII-Denis Diderot ;  Agence Nationale de la Recherche [asSUPYCO, ARNr-QC  to C. C. and BaRR to S. D. ]. This work has been published under the framework of  Equipex (Cacsice)  and a LABEX programs (Dynamo) that benefit from a state funding managed by the  French National Research Agency  as part of the  Investments for the Future program. Funding for open access charge:  ANR grant ARNr-QC.
Inhalation exposures For inhalation experiments, mice were placed 4 h/day, 5 days/week for 2 weeks and for one additional day in a ventilated whole-body inhalation chamber that allowed free movement (InExpose, Scireq®)  [21]. Nebulization was achieved using an Aeroneb Lab™ ultrasonic nebulizer directly connected to a 5 l-chamber and controlled through the flexiWare software v. 6 according to the following parameters: bias flow of 2 l/min, nebulization rate of 0. 083 ml/min, which were measured and controlled throughout the experiment. The Aeroneb Lab™ ultrasonic nebulizer produced droplets with a volume median diameter of 11 μm characterized by laser diffraction using the Spraytec system from Malvern Instruments. Mice were exposed to a solution of 40 μg cPM D /ml or to the soluble or insoluble fractions of cPM D.
Methods Field procedures. We studied a single colony of house martins in Badajoz, Southern Spain (38°53'N 7°01'W), during 2005-2013 (excluding 2006). From February to July, we followed reproductive events every second day to determine laying date (i. e. , the date the first egg is laid) and brood size (i. e. , the number of fledglings in the first brood). Adult house martins were captured and identified with numbered metal rings. Our original sample size was 195 adults and we only took one observation per individual. We categorized our original data in two different age-classes: young birds (i. e. , one-year old individuals that have migrated for the first time that year; n = 129), and experienced birds (i. e. , two-years or older individuals that were at least in their second migration year; n = 66).
We found in a previous study that breeding phenology (i. e. , first egg laying date) depends on wintering area inferred from stable isotope analysis  7. It is also well known that breeding phenology is related to reproductive success in migratory passerines  6, 10. Different studies have found an effect of wintering habitat (inferred from δ 13 C values) on body condition before departure to the breeding areas  2, 4  , and during spring migration  3. Therefore, we included as hypotheses to be tested causal paths from winter-grown feather δ 2 H, δ 13 C and δ 15 N to body condition during the breeding season. We measured body condition when we first captured the individual. Within our original data set, 140 birds were first captured after they had already started breeding, whereas 55 birds were first captured before they started breeding.
Acknowledgements This study was funded by research projects of the  Spanish Ministry of Economy and Competitiveness  ( CGL2015-64650-P ),  Junta of Extremadura  ( IB16121 ),  Junta of Andalucía  ( P12-RNM-2144 ) and  University of Extremadura  ( A7-26 ). CLC was supported by an operating grant from the  Junta of Andalucía  ( P12-RNM-2144 ). AM and MR were supported by grants from the  Spanish Ministry of Education and Science  ( JC2011-0405 ,  BES 2004-4886 ,  BES-2007-15549  and  BES-2010-030295 , respectively). Isotope analyses were supported by an operating grant from  Environment Canada  to KAH. We want to thank  C. Gryba  and  G. Koehler  who assisted with stable isotope analyses.
Introduction The DNA-barcoding initiative  (Hebert et al. 2003)  and Next-Generation Sequencing technologies have dramatically increased the amount of molecular data available for biodiversity studies. It is now common to manipulate datasets of 100s or even 1000s of haplotypes for a given genetic marker. Typically, DNA barcodes obtained from several specimens in several species are compared to test whether intra-and interspecies diversity matches (or not) the accepted species hypotheses, which are generally based on morphological characters. Such comparisons will eventually lead to detecting highly structured groups within a given species, then used as primary species hypotheses in an integrative taxonomy approach  (Dayrat 2005). There is a need for efficient bioinformatic tools to handle increasingly larger datasets, and many tools have been proposed to turn raw data in a wide range of input formats for analytic software.
While some contaminations are easy to detect (e. g. , contamination by very distant organisms), others are more tricky to identify (e. g. , contamination by a closely related species). The BLAST tool in GenBank is commonly used to detect such contaminations, but many species are not represented in GenBank, and detecting contaminations often requires comparing sequences from closely related species. Thus, one way to efficiently detect such discrepancies is to visualize the data with a phylogenetic tree and compare the phenotypes of the sequenced specimens in the order they appear in the tree. While numerous standalone programs are available to visualize phylogenetic trees (e. g. , FigTree  (Rambaut & Drummond 2009) ; Treeview  (Page 2002) ; MrEnt  (Zuccon & Zuccon 2010) ;…) none of them offer, to our knowledge, the possibility to automatically display pictures associated to the sequenced specimens in a way that enables a direct comparison of a tree and hundreds of associated images.
In our case, this gap in the analytical workflow appeared when we started to accumulate barcoding data from the specimens preserved in the collection of the MNHN (Muséum national d'Histoire naturelle) and stored in its databases  (Puillandre et al. 2012). Sample contamination occurred regularly because we were working with universal primers on specimens that were potentially not perfectly preserved and collected over several decades. To allow a rapid identification of conflicts between DNA phylogenies and morphological characters, we present the program TreePics as a webtool and as a standalone software. TreePics can be used to visualize a tree in Newick format and, at the tip of each branch, a picture corresponding to the specimen (typically, a picture of the voucher, but potentially any picture related to it, a detail of the voucher, an icon related to a phenotypic trait,…).
It is not designed to edit a tree, and can thus be used in combination with other programs that are dedicated to tree editing. The ETE toolkit  (Huerta-Cepas et al. 2016 ) is also able to associate pictures to branch tips, but it needs to be used within a Python framework, where pictures and taxa names are associated manually. TreePics has been designed to automatically associate hundreds or thousands of pictures to a phylogenetic tree, using only a web browser.
General functioning TreePics is accessible through a web browser. It is based on the web standards HTML5, CSS3, javascript and the library jQuery. The input tree in the Newick format is converted in the SVG (Scalable Vector Graphics) format using the library jsPhyloSVG  (Smits & Ouverney 2010)  to obtain a vector graphic representation of the tree which is understood natively by modern browsers. All operations in the web browser are performed in javascript, and all the operations are done locally in the web browser. TreePics is thus independent from any application server, and the pictures do not need to be uploaded (either on the server or on cloud storage). Because the size of the pictures can easily reach several gigabytes, this step would have been highly time-and space-consuming. If a web application is not constrained by the heterogeneity of the operating systems, it must nonetheless take into account the security rules imposed by the web browser.
2. A full standalone version (downloadable at https://treepics. mnhn. fr) can be installed locally. The process is here divided into three steps: in addition and before the two steps of the web version, the user needs to provide the path to the folder that contains the pictures. As in the web version, the operating system, and not the web browser, will manage the local resources. However, the web browser obtains the data stream (base64) or a list of file names, but not the local path, which needs to be user-specified. This adds a step to the process compared to the web version, but the standalone version does not necessitate an internet connection and reduces the memory needed. The standalone TreePics also exists in French and English and the language is requested when downloading.
Data input and visualization Two types of files are provided: the pictures, in JPEG or PNG format, and the tree, in Newick format (https://en. wikipedia. org/wiki/Newick_format). Values for the tree nodes (typically, statistical support) provided in the Newick tree will be displayed by TreePics. An example (a tree with the associated pictures) is provided in the supplementary material 1. To associate the pictures to the names of the taxa in the tree, TreePics will compare the names of the pictures and the names of the taxa in the tree. The rule is simple: the name of the picture should be included entirely (without the extension) in the name of the taxon in the tree. The name of the picture should contain only alphanumeric characters, dashes ("-") and underscores ("_"); in addition to the constraints linked to the OS (e.
Restrictions Because TreePics uses a web browser, the problems linked to the different operating systems are minimized, but not totally removed. In particular, the classic rules for naming the files should be followed (e. g. , no space on Mac). We tested several web browsers: while Google Chrome makes better use of the memory than Firefox, it also limits the number of files that can be selected (according to their size) compared to Firefox. Using a PC with 16GB RAM and Firefox, we were able to visualize a tree with 3000 pictures. In any case, the full standalone version is more robust and should be preferred for large datasets.
Acknowledgments TreePics is the result of discussions with the people working with the INVMAR MNHN collections. The authors thank all the people who produced and analyzed the huge amount of data DNA sequences and pictures recorded in the MNHN databases, from which the need for TreePics emerged. In particular, we thank the  3E team (Exploration, Espèces Evolution ) of the ISYEB, the INVMAR team that manages the collections, the  SSM (Service de Systématique Moléculaire ,  UMS 2700 CNRS-MNHN ) where most of the DNA data used to test TreePics where produced, and the  DSI (Division des Systèmes d'Information)  of the MNHN. We also thank the team of testers who tracked the bugs in TreePics, and  Mark Phuong  for his help in correcting the English. This project benefitted from the  CONOTAX  project, funded by the  French ANR  ( ANR-13-JSV7-0013-01 ).
Application of the model in Amazonian farms The data were collected during three years between 2011 and 2014, through a questionnaire in order to get the quantities of inputs used and outputs produced on an average year on each farm. In the Amazonian context, the inputs corresponded to electricity and fuel as direct inputs and animal purchase, animal food, fertilizers, installation and equipment as indirect inputs. The outputs were the animal products (beef, milk and/or cheese) and the calf purchase. Twenty-two (  22 ) farms, located at the Eastern Brazilian Amazon region, which are representative of the area as described by  Poccard-Chapuis et al. (2005) , were studied. Among these farms, two groups of farms were identified: Ten (10) low mechanized smallholder mixed dairy-beef farms (SM) producing essentially for local consumption and sell the surpluses of milk in the form of cheese at the local scale and the male calves to the beef fattening farms.
Adaptation to the Amazonian context The technical practices, the energy coefficients of inputs and outputs, were adapted to the context of the Amazon region, in a new software called ALGEBRA, developed by the "French agricultural research for development" (Cirad) and Agro Paristech, with the cooperation of the Embrapa Amazônia Oriental. The enteric emission factors used were obtained from case studies from southern Brazil  (Braz Pereira et al. , 2002; Lima et al. , 2007). In the absence of Brazilian emission coefficients for the direct emissions of N2O from tropical soils, they were estimated from the literature existing for tropical regions worldwide and international study values of Ges'tim (2010) and Dia'terre  (2011). Some items were added to the balance spreadsheet in comparison to the Planet method (2002), including GHG emissions by the use of fire to burn forest areas or to manage pastures.
Statistical analysis The data were analyzed with the mixed model procedure (SAS. 2013. SASOnlineDoc® 9. 1. 3. in SAS Inc. , Cary, NC, USA). The univariate procedure of SAS was used to test for normal distribution of the data. The data were analyzed as a completely randomized design, with farm system as a fixed effect. Differences among means were tested using LSMEANS with the PDIFF option (SAS 2016), with significance declared if p-value ≤ 0. 05. Six variables were analyzed to compare the three production systems: the total energy outputs and inputs, the direct and indirect energy inputs (koe ha -1 of pasture), the energy efficiency (no unit) and the emissions of GHGs (kg of CO2 eq ha -1 of pasture). To compare the BB and BF systems, the following indicators were used: total energy inputs and outputs (koe t -1 of live weight produced) and the GHG emissions (kg of CO2 eq t -1 of live weight produced).
Data The study area was the Iberian Peninsula. Most of the time series were taken from an extensive database of daily rainfall time series provided by the Spanish National Meteorology Agency (AEMET). The quality requirements were to choose time series with no missing data and to cover the orographic diversity of the Iberian Peninsula. The final choice was a set of 76 daily rainfall time series corresponding to gauges as regularly spaced as possible over the IP. Their locations are shown in Fig. 1. The study period was 1961 to 2010. Most of the time series were selected from the daily rainfall time series of the Spanish National Meteorology Service (AEMET) according to the quality requirements described above. Four of the series were provided and quality controlled by the European Climate Assessment and Dataset service (ECA, available online at http://eca.
knmi. nl) and one series was from Portugal (Gafanha da Nazaré ) provided by the National System ofWater Resources Information (SNIRH, available online at http://snirh. apambiente. pt/, managed by the Portuguese Institute for Water) database. Data homogeneity was evaluated using the R-based program RHTestV3, developed at the Climate Research Branch of the Meteorological Service of Canada, and available from the ETCCDMI Web site (http://etccdi. pacificclimate. org/). This program is capable of identifying multiple step changes at documented or undocumented change-points. It is based on a two-phase regression model with a linear trend for the entire base series  [13, 14]. This analysis, together with the metadata of the stations, showed that none of the 76 time series had change-points significant at 5%, with all of them being homogeneous in the cited period of study.
Conclusions A study of the EVT to estimate non-stationary return levels of extreme rainfall for present and near future has been developed for the Iberian Peninsula. A complete dataset of 76 gauges for the period 1961-2010 was used. The two widely approaches in EVT, block maxima and peaks-overthreshold were used in order estimate the 20-year RLs in present time and the expected in 2020. Two approaches to computing future rainfall RLs with POT were studied. In the first, trends in the extremes considering all the days were identified, taking into account a time-varying threshold based on a linear quantile regression and, when appropriate, a trend in the GPD scale parameter. Then, in the second, we calculated the RLs considering only the rainy days, examining the impact of evolutions of the mean and variance and of the number of rainy days.