dataesr/scientific-paragraphs-categorization huggingface dataset:
Multiple imputation method and model We used multiple imputation by chained equation  [27, 28]  to create 20 multiply imputed datasets. Incomplete variables were imputed under fully conditional specification  [29]  because of its flexibility to specify the method and the set of predictors to be used for each incomplete variable. Calculations were made in R 3. 0. 2 [30]  using the mice package  [31]. The main variable of interest to impute was the variable cause of death, with 50 % missing data. All variables available and the related number of missing values are displayed in Table  1. They were all imputed; 4,961 out of 23,272 records (21. 3 %) were incomplete. PSA at diagnosis, cTstage, cNstage, cMstage, Gleason, first treatment, and PSA after treatment were imputed with the default method in mice.
Finally, Brand and Van Buuren  [32, 34]  have shown that MICE algorithm can converge with just 5 iterations. However, as some applications can require more iterations and as computations are not tedious, we set the number of iterations for this imputation model at 20. Multiple imputation with m = 10, 20 and 30 multiply imputed datasets were performed. Mean relative efficiencies for Kaplan-Meier estimator were calculated on each m and were equal to 0. 97, 0. 99 and 0. 99, respectively. Survival analysis were compared on multiple imputation with 10, 20 and with 30 multiply datasets, there were no differences between the curves and between the confidence intervals. Since it is recommended to set the number of multiple imputations to the average of missing data  [35]  and there were 21 % of missing values, m = 20 datasets were kept.
Survival analysis Three survival estimators were compared: Pohar Perme's estimator, Kaplan-Meier estimator and cause-specific cumulative incidence estimator. Pohar-Perme's net survival was estimated on the original data base without imputation. This method does not require the causes of death: it uses the mortality rates of the general population to estimate the survival. Pohar-Perme's net survival was calculated with the function rs. surv of relsurv package  [36]. The next step was to analyse the 20 multiply imputed datasets with a cause-specific survival approach. Causespecific survival was firstly estimated using Kaplan-Meier estimator when censoring other-cause deaths on each dataset separately, with the survfit function of survival package  [37]. However, the cause-specific estimates that are based on the imputation model can be interpretable as estimates of net survival only if it is reasonable to assume independence between prostate cancer death and death from other causes than prostate cancer.
First method We tested our multiple imputation model on a complete dataset, from which we randomly removed different percentages of causes of death. For this, we created a sub database with all the 161 dead patients with a known cause of death (on the 322 initially deceased patients) and with half of patients still alive randomly selected, in order to have a proportion of dead/alive patients similar to the one for the whole database. Thereby, the created sub-database was composed of 1422 patients, 161 of whom had died and a cause of death was identifiable. We could thus calculate the true causespecific survival. Of these 161 patients, 10 %, 30 %, 50 %, 70 % and 90 % of causes of death were randomly removed and the variables were imputed with the same imputation model as before. As the variables were randomly removed, MCAR hypothesis holds.
Second method In order to test our imputation model, we also considered the 20 first multiply imputed datasets from the original data (2844 patients). For each imputed dataset, the 161 original causes of death present in the original data (106 deaths from other causes and 55 cancer deaths) were deleted, and the 161 imputed causes of death were retained. Thus, the MAR assumption still holds if the original missingness mechanism is MAR. Then, the 161 deleted causes of death were imputed using the same imputation model as before. Twenty new multiply imputed datasets were created, for each first multiply imputed dataset.
Our results are satisfactory even when applying a 50 % missing rate for causes of death, because the MAR hypothesis holds for the variable causes of death. Moreover, with the first method of sensitivity analysis, on the sub database of 1422 patients, the multiple imputation model correctly estimated the missing causes of death as up to 50 % missing and the results of the Gelman's cross-validation  [42]  validated our imputation model. However, depending on the objectives, this method may not be applicable to very large databases for example, since the multiple imputation method is demanding in terms of time resources. Nevertheless, it is affordable when databases are not too large.
Indexing patterns Each scan is composed of 760 images. Grains are characterized by a collection of diffraction spots which can be indexed on a single lattice. For each observed spot on the images the scattering vectors were computed and these were grouped into hkl reflection families using the cell parameters input from the known crystallographic structure for austenite. Trial orientations for potential grains in the sample were found from pairs of observed spots which are separated by the expected angles for their hkl families. Orientations were retained when they predict a large number of diffraction spots. The center of mass positions (CM) of the grains and their orientations were then optimized. The calculations were all performed using the ImageD11 [12] software package. The grain volumes were estimated from the diffracted intensity.
A new development was needed to match the grains between two loading points so as to follow the same grain during the whole loading experiment. A calculation code was developed to analyze grains maps obtained from ImageD11 in order to relate them and be able to follow each individual grain during the mechanical cycle. Two solutions can be used: identifying grains by their orientation or by their spatial position in the specimen. The algorithm searches for the grains with the nearest position and/or the nearest crystallographic orientation of one grain between two maps (i. e. two loading points); in other words, two grains in two different maps are considered to be the same if the misorientation and the distance between their CM are below threshold values. We have matched grain maps considering both factors separately or together; the final grain pairing was always the same, showing the effectiveness of the algorithm for these data.
McClear, a fast model based on a radiative transfer solver, exploits the atmospheric properties provided by the EU-funded MACC project (Monitoring Atmospheric Composition and Climate) to estimate the surface downwelling solar irradiances for cloud-free instances. This article presents the first validation of the McClear model for the specific climate of the United Arab Emirates where skies are frequently cloud-free but turbid. McClear accurately estimates the global horizontal irradiance measured every 10 min at seven sites. The bias ranges from À9 W m À2 (À1% of the mean observed irradiance) to +35 W m À2 (+6%). The root mean square error (RMSE) ranges from 22 W m À2 (4%) to 47 W m À2 (8%) and the coefficient of determination ranges from 0. 980 to 0. 990. Estimates of the direct irradiance at normal incidence exhibit an underestimation that is attributed to the overestimation of the aerosol optical depth in the MACC data set and not accounting for the circumsolar radiation in McClear.
, 2012a (Oumbe et al. , , 2013)). The GHI and DHI were measured respectively by one unshaded and one shaded Kipp and Zonen CMP11 pyranometers (http://www. kippzonen. com) that comprise a thermopile covering the spectral range from 285 nm to 2800 nm. The DNI was measured by the Kipp and Zonen CHP1 pyrheliometer that comprises a thermopile covering the spectral range from 200 nm to 4000 nm. The sampling rate of the instruments was 1 s. Measurements were averaged over 1 min. To match the temporal step of 10 min of the six Masdar stations, measurements from the Bertin station and the McClear estimates were averaged over 10 min provided all 1 min measurements within the period were presenti. e. no missing values-and valid according to the quality check procedures of  Roesch et al. (2011). Then the algorithm of  Long and Ackerman (2000)  was applied to these 10 min data sets in order to retain reliable cloud-free instants.
The first and third tests of Long and Ackerman, namely the normalized total shortwave magnitude test and the change in magnitude with time test, were kept unchanged. In the second test, the maximum diffuse shortwave test, the maximum DHI was empirically set at 300 W m À2 , instead of the 150 W m À2 suggested by Long and Ackerman, to better represent the local conditions of the UAE. The fourth and final test, the normalized diffuse ratio variability test, was modified to accommodate for the 10 min time step instead of the 1 min time step of Long and Ackerman. The time span was changed from 11 min to 30 min and the threshold on the standard deviation of the normalized diffuse fraction was empirically set at 0. 02. As an average, 37% of valid data were retained as cloud-free. If G denotes the GHI, B the direct horizontal irradiance, B n the DNI, E 0 the horizontal irradiance at the top of the atmosphere and E 0n is the normal irradiance at the top of the atmosphere, the clearness index KT, also called the global transmissivity of the atmosphere, is computed as: KT ¼ G=E 0 : ð1Þ Similarly the direct clearness index (KT B ) is defined as: KT B ¼ B=E 0 : ð2Þ The direct normal clearness index KT Bn is also defined, but with respect to E 0n : KT Bn ¼ B n =E 0n : ð3Þ One notes that KT B = KT Bn because B = B n cos(h S ) where h S denotes the solar zenith angle.
It can be concluded that McClear provides accurate results of the GHI over the site AR. The slight underestimation may be due to an overestimation in the AOD 550 extracted from the MACC reanalysis data set, which will be discussed in Section 4. 1. The scatter density plot of the DNI estimated from McClear versus the ground-derived DNI is shown in Fig. 3. In this case there is an underestimation for most of the observations, both low and high values. The scatter around the best fit line is more noticeable than for the GHI. All statistical measures exhibit poorer performances than those for the GHI. The relative bias is À8%, the relative RMSE is 13% and R 2 is 0. 846. The bias is attributed to two causes. The first is an overestimation in the AOD 550 input to McClear. Aerosols directly affect the DNI but their effect is less pronounced on the GHI, which may explain the difference in the scatter observed in the two plots.
Comparisons with other modelled irradiances for cloudfree conditions McClear is now compared against two models from the literature predicting irradiances under cloud-free skies in order to further evaluate its performance over the UAE. The first one is the clear sky model of the European Solar Radiation Atlas (ESRA), which is used in the Heliosat-2 model  (Rigollier et al. , 2000). This model is an empirical one, based on a database of climatological monthly means of Linke turbidity. The GHI from the ESRA model is available in the HelioClim-3v4 (HC3v4) database (Blanc   (Eissa et al. , 2013). The E-ANN model does not require any aerosol information, but it does require a training beforehand. In this case it was trained using the same training set used by Eissa et al. made of observations in three stations for the year 2010 in the UAE.
Acknowledgments This work partly took place within the  PREDISOL  project funded by  Total New Energies. The research leading to these results has partly received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 218793  ( MACC project ) and no. 283576  ( MACC-II  project). The authors thank the team developing the radiative transfer model libRadtran (http://www. libradtran. org). The authors also thank  Masdar Clean Energy  and  Bertin Technologies  for the solar radiation measurements across the  UAE  and all the personnel who installed and maintained the AERONET stations in the UAE. The authors also thank the anonymous reviewers for their valuable comments which improved the quality of this work.
Molecular identification of species has seen a considerable and rapid development over the past decade following the introduction of DNA barcoding by  Hebert et al. (2003) ; during this period, the field has experienced extensive testing in a large variety of organisms, including many insect orders. DNA barcode libraries are being developed at a steady pace, combining genetic data (usually the sequences of the genetic marker used as the standard DNA barcode in animals: a 658bp fragment of the mtDNA COI gene, although additional markers are sometimes used to complement it), taxonomic information, and specimen data (collecting information, voucher repository, images). A global online database, the Barcode of Life Datasystems (BOLD), serves as the central repository for these libraries (www. boldsystems.
org) and combines classical database features with a workbench facilitating data analyses and data sharing  (Ratnasingham and Hebert 2007). At the same time, advances in NGS technologies have increased by several orders of magnitude the yield and throughput of DNA sequencing and triggered the development of metagenomics. Multiple genomes can now be extracted, amplified and sequenced simultaneously, allowing for the sequencing of environmental (air, water or soil for instance) or bulk (complex assemblages of multiple individuals) samples  (Shokralla et al. 2012 , Tautz et al. 2010). By targeting a DNA marker that permits species identification, like DNA barcodes, this method can be used to document the species composition of complex samples, like communities, in an approach called DNA metabarcoding  (Hajibabaei et al.
2011 , Taberlet et al. 2012 , Yu et al. 2012 , Cristescu 2014). The PASSIFOR project, initiated by the National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA) and by the National Institute for Agricultural Research (INRA), aims at developing a DNA metabarcoding approach for French species of saproxylic beetles to facilitate and enhance the use of these insects as forest indicators. As a first step toward that goal, we present and release in this paper a DNA barcode reference library for these insects, including 656 records representing 410 species in 251 genera and 40 different families. This library represents about 16% of the national fauna and we expect that its development in the next future will further contribute to the assembly of a DNA barcode library for European beetles.
Only a few species complex or notoriously difficult genera (e. g. Ampedus in family Elateridae) were more densely sampled. The PASSIFOR library uses the standard DNA barcode for animals, i. e. a 658bp fragment of the COI mitochondrial gene. Species identifications were provided by expert taxonomists for these groups. All records were initially identified on the basis of morphological examination, and voucher specimens are preserved in the collections of the taxonomists as references for these records. Any future change in the taxonomy/nomenclature of these insects will be reported in the PASSIFOR library, after authoritative validation by the taxonomists.
Project description Title: PASSIFOR: stands for (in French) "Proposition d'Amélioration du Système de Suivi de la bIodiversité FORestière": Proposal toward improving monitoring of forest biodiversity. Funding: This project is supported by a grant from the French Ministry of Agriculture (MAAF) to IRSTEA (CB) and INRA (CLV). Sequencing of DNA barcodes also benefitted from funding by Genome Canada and the Ontario Genomics Institute (OGI) to the International Barcode of Life Project (iBOL).
Study extent: The PASSIFOR library focuses on French species within 40 different families of saproxylic beetles. Sampling description: Tissue samples for DNA extraction were collected mostly from dry collection specimens; only a limited number of samples were preserved in 95%-ethanol. All specimens were photographed and specimen data were compiled in excel spreadsheets for submission to BOLD. Most specimens were sampled by RR and CLV in the National Laboratory for Forest Entomology, Quillan, France. GP, TB and BN assisted in sampling and in databasing records in their institutional collections. NM sampled specimens in his own reference collection, while JD selected and shipped a selection of specimens (Elateridae, especially members of genus Ampedus) to INRA Orleans where RR handled tissue sampling, photography and databasing.
Specimen sampling and data compilation: • tissue sampling. Using flame-decontaminated forceps, we usually pulled one leg from each specimen sampled. Occasionally, when these appendages were difficult to reach, we used the antenna or hindwing of the insect. For the smallest species, we sometimes used up to three of these body parts (usually several legs). Only in one case, a tiny representative of the Scolytinae Ernoporicus fagi, did we use the whole insect and as a consequence did not preserve any voucher specimen. • photography. Each specimen was photographed individually along with a scale. • data compilation. We used standard BOLD spreadsheets to compile: ▪ voucher information: SampleID (a unique BOLD identifier for the specimen; also added on a label pinned with the voucher specimen) and institution storing.
▪ Taxonomy data: higher level taxonomy; species identification; identifier, including contact information. ▪ Specimen details: sex (when available); reproduction mode; life stage; type of tissue used (for most specimens); collecting method (when available). ▪ Collection data: collectors; date collected; country; administrative region and department; sector; exact site; latitude, longitude and elevation (when available). • upload to BOLD. Following the standard BOLD procedure for DNA barcode library construction, we created a dedicated project in BOLD. This project (code PSFOR, publicly accessible) hosts records for all the samples processed (including failures), whereas the actual PASSIFOR library (dataset DS-PSFOR01, see the Data resources section below) only includes records successfully sequenced and subsequently validated by taxonomists.
Taxonomic coverage Description: The PASSIFOR library comprises 656 records for saproxylic beetles belonging to 40 different families. They represent 410 species in 251 genera. The nomenclature used generally follows that in the eight volumes of the Catalogue of Palaearctic Coleoptera series, edited by Löbl and Smetana (see f. i Löbl and Smetana (  2003 )), which in turn was largely followed, for French beetles, in the recent national catalogue by Tronquet (2014). New names and nomenclatural changes after publication of the volumes of the Löbl & Smetana catalogue were sometimes adopted in the PASSIFOR library, but only if they did not conflict with other DNA barcode libraries for these insects, or if they are considered consensual within the community of coleopterists involved in the construction of these libraries.
Analyses of genetic distances All sequence analyses were carried out in BOLD using Kimura-2 parameters (K2P) distances with BOLD handling the sequence alignment. Alternative alignment methods were tested (including the use of sequences aligned "as uploaded") and proved to have no impact on the results. All 656 sequences of the library where used to build a Neighbor-Joining (NJ) tree as illustrated in Suppl. material 3. For the analysis of intraspecific and interspecific distances, we reduced the dataset to sequences longer than 400bp (597 records, 388 species). General summary statistics at the species, genus and family levels are given in Table  3 ; Fig. 2  shows the frequency distributions of genetic distances within species (normalized) and within genus. Fig. 3  represents the distribution of maximum intraspecific distances (singletons excluded) plotted against distances to Nearest Neighbour within the library.
Overall, we observe a conspicuous bimodal pattern suggesting the existence of a marked "barcode gap" between intraspecific and interspecific genetic divergence. We note however that in the vast majority of species our sampling remains too limited, both taxonomically (sister species often unsampled) and numerically (intraspecific divergence undocumented for most species) to test the extent of this gap and its consistency. Frequency distribution of within-species (normalized, in pink) and within-genus (green) K2P distances for records of the PASSIFOR library (sequences longer than 400 bp only: 597 records, 388 species). Table of distances is provided as Suppl. material 4 and Suppl. material 5.
Discrepancies between current taxonomy and DNA barcode results While we are aware of the limitation of our dataset to address taxonomic questions in cases where DNA barcodes and current taxonomy reveal a possible discordance, we report here two categories of apparent conflicts between the results from DNA barcode analyses and species identifications derived from morphology. 1. High intraspecific divergence (>2%) were observed in 7 species ( Low interspecific divergences (<2%) were observed in 6 pairs of species, 1 triplet, and 2 pairs of subspecies (Table  5 ). In total, of the 410 species sampled in the PASSIFOR library, 15 (3. 6%) fall in this category of low to null interspecific distances. Here again, these cases will require additional sampling and further investigation to understand if our results reflect cases of overlooked synonymy (as may be the case in the pairs Ampedus pomorum / A.
license: Open Data Commons Attribution License Data resources Data package title: PASSIFOR DNA barcode reference library Resource link: http://dx. doi. org/10. 5883/DS-PSFOR01 /www. boldsystems. org/index. php/Public_BINSearch? searchtype=records Data format: xml, tsv, fasta, ab1 Description: The PASSIFOR library dataset can be downloaded from the Public Data Portal of BOLD in different formats (data as xml or tsv files, sequences and trace files as fasta and ab1 files). Alternatively, BOLD users can login and access the dataset via the Workbench platform of BOLD (see the public dataset list in the User Console page, under the name of first author); all records are also searchable within BOLD using the search function of the database.
Acknowledgements We are thankful to  Benoît Dodelin  ( Lyon, France ) for providing additional material in genus Ampedus, and to  Hervé Brustel  and  Lionel Valladares  of the  PURPAN Engineering School (Toulouse, France ) for their discussions of taxonomic questions. We also gratefully acknowledge the support from colleagues at the  Canadian Centre for DNA Barcoding  ( University of Guelph, Ontario, Canada ), and in particular  Natalia Ivanova ,  Jayme Sones  and  Evgeny Zakharov  who carefully and efficiently supervised the processing of the material analyzed in the present work. Rougerie R  et al.
Alternative license: Open Data Commons Attribution License Data resources Data package title: PASSIFOR DNA barcode reference library Resource link: http://dx. doi. org/10. 5883/DS-PSFOR01 /www. boldsystems. org/index. php/Public_BINSearch? searchtype=records Data format: xml, tsv, fasta, ab1 Description: The PASSIFOR library dataset can be downloaded from the Public Data Portal of BOLD in different formats (data as xml or tsv files, sequences and trace files as fasta and ab1 files). Alternatively, BOLD users can login and access the dataset via the Workbench platform of BOLD (see the public dataset list in the User Console page, under the name of first author); all records are also searchable within BOLD using the search function of the database.