dataesr/scientific-paragraphs-categorization huggingface dataset:
The NOEC community was set to 5 mg L À1 of dissolved copper for 2003 (Table  4 ). There were no taxa with a negative weight (Figure  11B ). Taxa with a positive weight, such as Orthocladiinae and Corynoneurina (Figure  11C  and D ) increased in the treated mesocosms. The NOECs for the taxa Chironomini, Corynoneurina, and Orthocladiinae are presented in Table  4  and are shown to vary with time. The dynamics and potential recovery of each population will be discussed below. The raw data are available in the Supplemental data (SI-3, Emerging insects raw data).
When we considered all of the results, the NOEC for consumers was set to 5 mg L À1 of copper (nominal concentration, 4 mg L À1 as average effective concentrations), and the LOEC was 25 mg L À1 (nominal concentration) or 20 mg L À1 (average effective concentrations). Using the Bio-Met tool, a PNEC of 5. 5 mg L À1 was estimated using our experimental conditions. This value is slightly lower than the PNECs of 6. 9 mg L À1 and 6. 8 mg L À1 estimated with our experimental conditions using the full European Union normalization procedure and the US Environmental Protection Agency's BLM, respectively. Supplemental Data-The Supplemental Data are available on the Wiley Online Library at DOI: 10. 1002/etc. 3822.
Acknowledgment-This research project was funded by the  French Ministry of Ecology and Sustainable Development (BRCD ,  011111 ). No authors have conflicts of interest to report. The authors are greatly indebted to  P. Baudoin ,  E. Guinard ,  E. Martin , and  S. Lamothe  from  INERIS  for technical assistance and to  F. Azemar  from  LEH  for helpful comments on zooplankton. We are also very grateful to the following students:  S. Borghi ,  F. Câtel ,  C. Achin ,  J. Robert ,  A. Marque , and  J. Saillard. We thank  T. Schmidt  and 3 anonymous reviewers for their constructive criticisms. Disclaimer-The use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by either the French or US governments. Data availability-Supporting data are available through the figshare digital data repository at https://doi.
Plant litter represents a major basal resource in streams, where its decomposition is partly regulated by litter traits. Litter-trait variation may determine the latitudinal gradient in decomposition in streams, which is mainly microbial in the tropics and detritivore-mediated at high latitudes. However, this hypothesis remains untested, as we lack information on large-scale trait variation for riparian litter. Variation cannot easily be inferred from existing leaf-trait databases, since nutrient resorption can cause traits of litter and green leaves to diverge. Here we present the first global-scale assessment of riparian litter quality by determining latitudinal variation (spanning 107°) in litter traits (nutrient.
Green leaves are typically poorer in nutrients in the tropics than at higher latitudes  26, 27  , and possibly better defended against herbivory  1, 2  (but see ref. 28). However, the very few studies that have explored litter traits globally have found that traits of litter can differ from those of green leaves  29  , partly because of differences in nutrient resorption efficiency across latitudes  30. This highlights the importance of quantifying trait variation of litter, rather than assuming that patterns for green leaves also pertain to litter. The most comprehensive study of litter trait variation, which used a dataset of 638 plant species across 6 biomes, showed that litter from tropical forests had higher nitrogen (N) but lower phosphorus (P) than litter from other biomes  30. The other two existing global studies examined a wider range of traits, but included a limited number of species.
P was determined on the filtrate by the molybdate-blue method, and absorbance was measured at 880 nm on a Jenway 6715 UV/Vis spectrophotometer  58. Additional leaves were rehydrated and ten 12-mm diameter discs were cut with a cork borer; the discs were then oven-dried at 45 °C for 48 h and weighed to determine SLA as the ratio of disc area (cm 2 ) to leaf dry mass (mg). Environmental and spatial variables. We extracted several climatic variables from the WorldClim database version 1. 3  59  at the highest resolution (30 arc-seconds) using DIVA-GIS v7. 5. 0. 0 (www. diva-gis. org) for each study site. These variables represented mean values and variability of temperature and precipitation for each site [mean annual temperature (MAT, °C), mean annual precipitation (MAP, mm), and temperature and precipitation seasonality (TS and PS, respectively, estimated as the standard deviation of monthly mean values × 100)] and an inverse proxy of aridity [precipitation of the driest quarter (PDQ, mm)].
We extracted the soil class  60  , soil pH, and soil organic carbon content (OC; g kg -1 ) for each study site from the Soil Grids database (www. soilgrids. org; resolution: 120 arc-seconds), and a value of soil N concentration (kg m -2 at 0 to 30 cm depth) was assigned to each study site depending on its soil class, based on Batjes  61  (resolution: 30 arc-seconds). We recorded the absolute decimal latitude (degrees from equator) and altitude (m asl) of each study site on Google Earth. Data analyses. Firstly, we used Principal Component Analysis (PCA) to visualize the variation of litter traits across species in relation to the environmental and spatial variables using JMP 9. 0. 1 (www. jmp. com). All variables were converted to z-scores using the 'scale' function in the base package of R software  62  , to standardize units and obtain slopes comparable in magnitude  63.
Correlation' function in PerformanceAnalytics R package) to identify and remove any collinear environmental or litter trait predictor (r ≥ 0. 60)  66. As a result of this inspection we excluded latitude, altitude, TS, PS, PDQ and soil OC, which were strongly correlated with several other variables. Given that collinearity between N and P (r = 0. 58) and MAT and MAP (r = 0. 63) was near the threshold, we calculated variance inflation factors (VIFs) for all the predictors to ensure they would not inflate the variance of models. As all VIFs were below 4, they were maintained 66 (Supplementary Table  S3 ). All predictors were converted to z-scores. Different residual spread within sites was allowed through the use of a variance structure ('VarIdent' function in nlme package  67  ; Zuur et al. 66  ), the need for which was defined based on Akaike's information criterion (AIC).
Finally, we used a backward selection procedure based on AIC to define the best model for each litter trait (Supplementary Table  S4 ). Briefly, we started with a model containing all individual predictors and the interaction between MAT and MAP (i. e. , the full model), then sequentially removed the least significant predictor (to obtain a reduced or nested model), and tested model improvement based on AIC (using the 'update' and 'anova' functions to remove predictors and to compare full and reduced models, respectively) until we reached a model where all predictors were significant  65. Models were fitted using the 'gls' (generalized least squares) function and restricted maximum likelihood (REML) method in the nlme R package. Regression plots were drawn with ggplot2 package  68. The relative contribution of each predictor to the model was estimated using bootstrapping ('lmg' metric with 'boot.
e. , species data) were considered as a random component, and the variance structure was the same as described above for linear models. The need for both components was defined based on AIC. Finally, we examined the phylogenetic signal of each litter trait -that is, the tendency for related species to share more similar trait values than species drawn at random from the phylogenetic tree  71. For that purpose, we used a previously constructed, fossil-calibrated tree of angiosperms 72 as a framework. We placed missing species in the tree next to nearest relatives using the tool 'leafbud. py' in Python 2. 7 (Supplementary Methods), employing a method similar to Phylomatic 73. Inserted species were assigned a branch length equal to their nearest relatives. Polytomies were randomly resolved using the tool 'ete3' in Python 2.
7  74. The new branch created by resolving the polytomy was assigned half of the length of the shortest child branch attached to the polytomous node. The child branches on the resolved nodes were adjusted to account for the added distance resulting in no change in the distance between any species and the root. We quantified the phylogenetic signal of litter traits using Pagel's λ  75  in the R package phytools  76  ; this index indicates stronger relationships between species traits and the phylogeny by the strength of its deviations from zero  77. Under a Brownian motion model, where species inherit their traits from ancestors but then slowly diverge by small random steps occurring at a constant rate, Pagel's λ is expected to be equal to 1, whereas values of 0 imply that there is no phylogenetic dependence  77.
Acknowledgements We thank the many assistants who helped with field work ( Ana Chará-Serna ,  Francisco Correa-Araneda ,  Juliana França ,  Lina Giraldo ,  Stephanie Harper ,  Samuel Kariuki ,  Sylvain Lamothe ,  Lily Ng ,  Marcus Schindler , etc. ),  Cristina Grela Docal  for helping with leaf chemical analyses, and  Fernando Hiraldo  (former director of  EBD-CSIC ) for his support. The study was funded by start-up funds from the  Doñana Biological Station (EBD-CSIC, Spain)  and from  Ikerbasque  to LB, the  Fundação para a Ciência e Tecnologia (FCT)  strategic project  ID/ MAR/04292/2013  granted to  MARE (Portugal) , the ' BIOFUNCTION ' project ( CGL2014-52779-P ) from the  Spanish Ministry of Economy and Competitiveness (MINECO)  and  FEDER  to LB and  J. Pozo , and  Basque Government funds  ( IT302-10 ) to  J.
Paduszynśki et al. 29  reported that each IL was composed of three basic elements: cation core, anion core, and substituents attached to the cores. Qiao et al. 18  selected 51 groups to establish the GCM. The density of an IL was a sum of the contributions of all included groups. The temperature and pressure dependences of contributions for each group were estimated respectively by correlation of 7381 data points for 123 ILs. However, only 188 data points of three ILs were used to test the quality of this model. This still needs further investigation to prove its applicability. Lazzuś et al. 17  expressed the reference molar volume of IL (298. 15 K and 0. 1 MPa) as a summation of the contributions of all involved groups. Thus, the reference density (298. 15 K and 0. 1 MPa) was calculated as the molecular weight divided by the reference molar volume.
DATA ANALYSIS First, we collected the experimental density data of ILs as a function of temperature from (217-473) K and pressure from (0. 1-207) MPa from the literature. The paper focuses on the estimation of ILs density at high pressure; hence, only the ILs, of which the density data at high pressure have been reported in the literature, were collected. A database was established containing over 16 092 experimental densities of 81 ILs. The database is provided in the Supporting Information. An overview of all the experimental data is presented in the Table S1 of the Supporting Information, which summarizes the references reporting the measurement densities for each IL, number of data points, temperature range, and pressure range. For the IL, of which its experimental density data were reported by several papers, mathematical gnostics was used to give an analysis and recommend one data set used to establish the group contribution model.
For the IL, of which its experimental densities, especially at high pressures, were reported by only one reference, recommended data could thus not be obtained by mathematical gnostics, and the given set was then considered as the recommendation data set. Mathematical gnostics is a novel axiomatic approach to uncertainty. 39 It stems from the theory of measurement and adopting analogies from the fundamental laws of nature such as special theory of relativity and thermodynamics it provides a solid basis for defining the theoretical model of uncertainty of individual data without the need of using a prior knowledge of a distribution function of experimental errors. Properties of a finite data sample are then obtained by aggregating the properties of all individual data. The data sample may be arbitrarily small because unlike in statistics, the extrapolation step from an infinite data set to a finite data sample is not used.
The MATLAB script is available in the Supporting Information (Filename: Tait_Parameters). The Tait parameters for all the other ILs are provided in the spreadsheet of the Supporting Information. As density property depends on the pressure and temperature, the analysis of density data by mathematical gnostics should be under identical conditions (i. e. , the same temperature and pressure). Herein, we calculated the density values at temperatures of 273. 15, 303. 15, 333. 15, and 363. 15 K and pressures of 0. 1, 20, 40, and 60 MPa from Tait correlations for each reference, respectively. Then mathematical gnostics was used to analyze data samples at each identical condition. The calculated densities values of [C 8 mim][BF 4 ] at these temperatures and pressures are provided in the spreadsheet.
In this case, mathematical gnostics is not applicable, and the statistical analysis is adopted. First, the experimental data collected from each reference were correlated to obtain the Tait parameters. Second, the experimental data from both references were correlated altogether to obtain another set of Tait parameters. Then the calculated densities from the Tait correlations for each reference were compared with the calculated densities from the Tait correlation based on both two sets to recommend one set of two. Gołdon et al. 47  and Aparicio et al. 48.
A second-degree polynomial, as shown in eq 6, was found to satisfactorily fit the variation of the ions effective molar volumes as a function of temperature: ∑ δ δ * = • = V T D T ( ) ( ) i i i ion 0 2 (6) where δT = (T -298. 15 K) and D i are the coefficients obtained by fitting the data at 0. 1 MPa: δ δ * = * -• δ δ + + ⎜ ⎟ ⎛ ⎝ ⎞ ⎠ V T p G H V T p G ( , , , ) ( , ) 1 ln H T p H T p ion ion ref ( ) ( ) ref (7) where V ion * (δT, p ref ) is the reference effective molar volume calculated using eq 6; p ref = 0. 1 MPa. G is an adjustable parameter, and H(δT) is a second-order polynomial:      S3  of the Supporting Information. Then the molar volume of any cation-anion combinations can be evaluated. The experimental data of other 27 ILs, which had not been correlated, were used to compare with the pure evaluated values.
■ ASSOCIATED CONTENT * S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acs. iecr. 7b00753. Data analysis figures for all other ILs (PDF) Experimental density data; Tait parameters, interval analysis, recommended data sets, calculated kappa and alpha values, and the calculation process of binary IL mixtures; summary of all experimental data collected; recommended data for each IL; volumetric parameters calculated; overview of experimental data; Matlab files (ZIP).
ACKNOWLEDGMENTS N. Z. and J. J. acknowledge gratefully the CEA le Ripault (Grant No. 4600261677/P6E31) and the Engineering and Physical Sciences Research Council (EPSRC) for supporting this work financially (EPSRC First Grant Scheme, Grant No. EP/ M021785/1). ■ ABBREVIATIONS Cations T = temperature (K) M = maximum location N = number of data points V m = molar volume of IL (cm 3 /mol) V experimental = molar volume experimentally measured V calculated = molar volume calculated by our method V cation * = effective molar volume of cation (cm 3 /mol) V anion * = effective molar volume of anion (cm 3 /mol) V ion * = effective molar volume of ion (cm 3 /mol) x i = correlated density (g/cm 3 ) Z o = maximum location of the global distribution function LB = lower bound of data support UB = upper bound of data support Z L = lower bound of typical data Z U = upper bound of typical data Z OL = lower bound of tolerance interval Z OU = upper bound of tolerance interval N Corre = number of data points used for correlation N Eva = number of data points used for evaluation Greek Letters.
DNA extraction, PCR and genotyping Genomic DNA for genotyping was extracted using the Instagene™ Matrix kit (Bio-Rad Laboratories Inc. , California, USA) following the manufacturer's instructions. Multiplex PCR were performed by pooling microsatellite primers into three pools of 4 pairs of primers per pool (Table  2 ). Multiplex PCRs contained 7. 5 μl of Type-it microsatellite PCR kit (2×) (Qiagen, Hilden, Germany), 0. 4 μl of each forward and reverse primers of the pool (10 μM), 1 μl of DNA sample and sufficient water for a 15 μl final reaction volume. Thermal amplification conditions were 95 °C for 5 min, followed by 35 cycles of 95 °C for 30 s, 58 °C for 45 s, 72 °C for 55 s and a final extension step of 72 °C for 10 min. PCR products were sized by capillary electrophoresis on an ABI prism 3130 (Applied Biosystems, California, USA) with GeneScan™ 500 LIZ® Size Standard (Applied Biosystems, California, USA) and GENEMAPPER software (Applied Biosystems, California, USA).
Statistical analysis Genotypic linkage disequilibrium between pairs of loci was tested with the exact test implemented in the GENEPOP v. 4. 3  [38]. The same software was used to estimate basic measures of genetic variability: mean number of alleles per locus (Na), observed (Ho) and expected (He) heterozygosities and F IS  [39]  for each population. Significance test for F IS values were performed using Hardy-Weinberg exact test implemented in GENEPOP. Allelic data were checked for null alleles, allelic dropout and stutter bands using MICROCHECKER v. 2. 2. 3 with the Oosterhout algorithm  [40]. Population differentiation (F ST ) was estimated using ARLEQUIN v. 3. 5. 2. 1  [41] ; the ENA method was used also to calculate an unbiased F ST (F ST ENA ) using FREENA  [42].
STRUCTURE HARVESTER  [45]  was also used to identify the most pronounced level of population structure using the method of Evanno et al. [46]. CLUMPP v. 1. 1. 2 [47]  was used to find the optimal alignment from replicate STRUCTURE runs, with the summary of results generated using DISTRUCT v. 1. 1  [48]. DAPC is a multivariate statistical method which uses the k-means clustering approach  [44]. DAPC first transforms data using a principal components analysis (PCA) and subsequently identifies clusters using discriminant analysis (DA). This method thus defines genetic clusters, assigns individuals to clusters and allows visual assessment between-population differentiation. DAPC was implemented using the adegenet package  [49]  of the R software  [50]. The identification of the number of genetic clusters was done using the function find.
For all demographic parameters, prior distribution ranges were implemented according to current knowledge of X. cheopis. The generation time of X. cheopis varies between 18 days to 20 months according to biotic and abiotic conditions  [52]. Laboratory conditions of 25 ± 2 °C temperature and 70-80% relative humidity stimulate an average 12 generations per year. However, we note that natural populations of X. cheopis show seasonal dynamics in both the Central Highlands and Mahajanga  [53]. Larval and pupal development times and survival can be affected by temperature and humidity  [53]  and so, generation times may be longer under Posterior probabilities of each scenario were computed by performing direct approach and a polychotomous weighted logistic regression on the 1% of simulated datasets closest to the observed dataset  [54, 55]  after linear discriminant analysis on summary statistics  [51].
Principal components analysis (PCA) was performed using summary statistics of simulated datasets and of observed dataset to check the suitability of the model (scenarios and prior parameters). Confidence in scenario choice was further tested using additional simulations. This included estimation of the probability of a type-II error (the probability of selecting the chosen scenario when it is not correct). After scenario choice, we proceeded to parameter inference estimated from the modes and 95% confidence intervals (CI) of their posterior distributions. To determine directional relative migration between the three chosen populations, we used the divMigrate function from the R-package diveRsity v. 1. 9. 89  [56]  using Jost's D as measure of genetic distance  [57]  and with a bootstrap value of 1000.
Funding Epidemiological surveys and laboratory works were funded by the  Agence Régional de la Santé -Océan Indien (ARS-OI)  (n°2/DSP/Etudes et Statistiques/ 2013) and the  Institut Pasteur of Madagascar. Microsatellite primer development was supported by the  Wellcome Trust  (a part of  Research Career Development Fellowship  to  S. Telfer  # 081705  and  Senior Fellowship  to  S. Telfer  # 095171 ).
Among all the names stored in the records of the state institutions, "the conceivers of the map take what they [emphasized by the interviewee] think are the most important names" (Lule Sami consultant), according to their own criteria. Thus the names already inventoried in databases are too numerous to be written on maps, and their continual renewal by the people who inhabit and use the land makes it practically impossible for mapmakers to integrate them all. Indeed, newly created names can arise from personal stories, sometimes amusing: (…) they named a small hill Piklesbacke, where they. … They were spotting moose from there, and it's known, it's known by all the people who ever go there, they know the name Piklesbacke. And the name is from … there is a tube which you can, like a tube of paste that you can have on a sandwich, it's called Pikles [from English "pickle"].
acknowledgMents We are grateful to the members of the Sami reindeer herding communities of Jokkmokk for their patience and their willingness to share their knowledge, and to Sees Editing for correcting the written English. Three anonymous reviewers gave constructive comments that contributed to improving the manuscript. Financial support was provided by the Agence Nationale de la Recherche funded program BRISK (Bridging Indigenous and Scientific Knowledge in the Arctic) (ANR-12_SENV-0005). references cIted.
These circumstances ultimately hinder the utility and longevity of the data as well as the ability to make scientific advances beyond those in the original study that generated them. Given the importance of U-series geochronology in several diverse disciplines and the continuing need to access, compile, and reinterpret data, there is a clear value to establishing baseline datareporting norms. This issue has been previously addressed in the context of general geochemistry data  (Goldstein et al. , 2003)  as well as for specific sub-disciplines (e. g. ,  Renne et al. , 2009). As with other geochemical techniques, advances in instrumentation and analytical methodologies have enabled the production of a greater volume of data, typically with improved precision and accuracy. Coupled with these developments is the increasing ability of authors to publish more extensive suites of data in online supplements that are linked to journal publications and in open-access, independently maintained community databases.