dataesr/scientific-paragraphs-categorization huggingface dataset:
The test outlined here is for the comparison of 2 datasets, for multiple datasets the t-test and signed rank test can be replaced by an ANOVA or a Friedman Repeated Measures Analysis of Variance on Ranks, with a Tukey post hoc test to identify differences. If the t-test or signedrank test does give a significant difference, the results of the two methods are subsequently assessed with consideration of the analytical uncertainty. For this, the absolute difference between results of method 1 and method 2 was calculated for each sample. Subsequently the standard deviation of the difference was calculated using equation 1: 𝜎 𝑑 = √𝜎 1 2 + 𝜎 1 2 (eq. 1) Where σd is the standard deviation of the difference between the analytical method 1 and analytical method 2, and σ1 and σ2 are the standard deviations of the results for method 1 and method 2 respectively.
Different samples approach As stated above, the above approach is limited to comparing results from the same samples or subsamples thereof and is not suited to compare oceanographic samples collected by different sampling systems or at different times, as samples will have been collected at (slightly) different depths and/or locations. Therefore a script was developed in the freeware program R (R Core Team, 2013) that interpolates oceanographic profiles and the analytical uncertainty and subsequently tests the shape of two profiles within a prescribed depth interval. The interpolation allows comparing a sampled depth on one profile with an interpolated value in the other profile. Besides, errors in interpolation on mean concentrations and standard deviation of each profile are used to determine the standard deviation of the interpolated values assuming a 95%-significance confidence interval.
No discernible difference was observed between the 'element dilution' and gravimetrical method (Fig. 1 ), either with a two-tailed paired t-test (p=0. 20) or the script for profile comparison that includes analytical uncertainty (p=0. 97). The benefit of the element dilution calibration technique is that the gravimetrical determination of the pre-concentration factor is no longer necessary, making it possible to do the time consuming extractions at sea. The results presented in this paper are based on extractions done in the shore based lab, but meanwhile the extractions for the subsequent Netherlands Mediterranean GEOTRACES expedition were carried out aboard RV Pelagia by the Middag group and analysed at the University of California Santa Cruz during a visit to the Bruland lab. These results are written up separately, but the results for the GEOTRACES reference samples that were extracted shipboard are included in Table  4  and are not distinguishable from the results for the here reported samples (P>0.
Intercomparison between the different sampling systems The BATS intercomparison or cross-over station (31°45. 92'N, 64°04. 95'W) was used to compare the sampling systems used by the Netherlands and US GEOTRACES programs for 12 dissolved trace elements. The data of all trace elements are attached in the supplementary material. Since a different number of samples were collected at different depths between the occupations, the R script is used to interpolate the profiles and assess the differences. Only data below the main thermocline was used (deeper than 1000 m) to avoid temporal variation between the two occupations that were over a year apart and had a negative and positive sea surface height anomaly during the Netherlands and US occupation, respectively, indicative of an up-and down-welling eddy (this was determined using the Colorado center for astrodynamics research SSH data viewer at http://eddy.
50). Dissolved Co, like Fe, displays characteristics of both a scavenged-type as well as a nutrient-type element. The latter is due to the nutritional requirement of phytoplankton for Co (e. g. Saito and Goepfert, 2008). For Co, the UV oxidation step is of utmost importance, as can be seen by the difference between the data from samples with and without UV oxidation (Fig. 2c ). Only one replicate was analysed for the UV oxidised samples and thus the analytical uncertainty was conservatively estimated at 2%. The Fisher law in the R script cannot be run when only one replicate measurement is available for both profiles. To allow comparison, two replicates for each data point were assumed (note that increasing the number of replicates makes the test harsher). With this assumed error, the profiles are not statistically different within analytical uncertainty (i-p-value=0.
93). Dissolved Ni is another nutrient-type element (Fig. 2d ) as it is used in the breakdown of urea  (Price and Morel, 1991)  and by nitrogen fixers (e. g. (Ho, 2013) ). The general agreement between the data of the Netherlands and US occupation is good, but the data from the US occupation showed four outliers (3000, 2500, 1800 and 874 m depth). Outliers were defined as described by  Middag et al. (2011a) , where a linear regression was determined between the data above and below the suspect data point and a deviation of more than 10% from the predicted value was used as the rejection criterion. The samples contaminated for Ni were also contaminated for Cu and Zn at a roughly constant ratio of Ni:Cu:Zn but were not contaminated for these metals in profiles analysed by a different PI in the US GEOTRACES program (Jingfeng Wu, pers.
A recent paper has shown that this method does work well for the suite of REE's  (Hatje et al. , 2014). The recovery for Ti was not quantitative at ~50% recovery, resulting in relatively poor precision and accuracy. The concentrations were calculated based upon the standard addition sensitivities assuming that all samples and standard additions had the same 50% recovery. Nevertheless, the vertical profiles of both occupations agree quite well (Fig. 3e ), and are not statistically different within analytical uncertainty (i-p-value=0. 80). Due to the low accuracy it might not be suitable to look at the actual concentrations, but the observed trends should represent actual variation in the ocean. However, one thing that is worth noting is that the data of the Netherlands occupation is not significantly higher than the data from the US occupation, indicating there is no noticeable contamination of Ti from the Netherlands titanium frame.
Multi-element standard addition ICP-MS versus shipboard flow injection methods The metals Mn and Fe were also analysed shipboard using flow injection methods. Both methods are based on chemiluminescence produced by the reaction between hydrogen peroxide and luminol which is catalyzed by Fe or Mn (see  Klunder et al. (2011) and Middag et al. (2011a)  for details). The vertical profiles, at first sight, demonstrate agreement between the shipboard methods and the standard addition ICP-MS method (Fig. 2a, 2b ), but for both metals there is a small discrepancy. For Mn, the profiles are not significantly different within analytical uncertainty using the R script (i-p-value=0. 05) but the t-test approach does give a significant difference within analytical uncertainty. The shipboard data is about ~0.
03 nmol kg -1 lower at all depths. A similar offset was observed for the analysed reference samples (Table  3 ), but results for both methods were within the consensus range. For the shipboard data the blank was undeterminable, so no blank was subtracted. For the ICP-MS data a small blank of 0. 04 nmol kg -1 was determined and subtracted from the data. Perhaps this blank is an underestimation, but that does not seem very likely since Mn is not a contamination sensitive element. Adjusting either dataset for the observed offset makes them undistinguishable when considering the analytical error using either test (i-p-value=0. 83 and p=0. 91 for the R script and t-test approach, respectively), implying that this indeed is an absolute offset. However, given that the offset is just insignificant based on the R-script, the deviation between the profiles roughly equals the analytical uncertainty.
Conclusion The intercomparison of the different Netherlands and US sampling system by analysing samples from the BATS cross-over station with the same method and analyst demonstrated that both sampling systems are capable of collecting uncontaminated samples for all the GEOTRACES key elements. Nevertheless, at all times throughout the procedure, utmost care must be taken to avoid inadvertent contamination. All results for reference samples are in agreement with the current consensus values for those elements that have a consensus value available. Moreover, the data reported here for trace metal profiles obtained with different analytical methods are consistent, demonstrating comparibility between techniques. This implies the analytical ICP-MS methods and the shipboard FI methods are functioning as expected and produce accurate data that can be reliably compared between cruises, with the caveat of Ga and Ti profiles, for which we did not yet achieve quantitative recovery.
This has been done for example by the CARINA program for ocean CO2 and ancillary measurements  (Olsen et al. , 2009). It should be realised though, that one cross-over station in a long section is not a definitive answer to resolve potential differences. It will only work if every data set is also validated by regular measurements of the international reference samples to ensure consistency within the international community and merged database. A remark here with respect to the reference samples is that these should not only be analysed, but the results also need to be submitted to the reference sample coordinator (K. W. Bruland; bruland@ucsc. edu) in order to have the most representative updated consensus values available to the community. Furthermore, since the consensus values are not certified or constant, no corrections should be made to datasets based on analyses of reference standards; the values should just be part of the metadata.
Internal seawater reference samples are most useful to identify any intra-dataset variability (i. e. variation between analyses performed on different days). The routine use of an internal seawater reference samples to be analysed far more frequently than the international reference samples is recommended, simply due to the scarcity of the international reference samples. Tables  Table 1 overview         for the p-value profiles). Note that the integrated p-value (i-p-value) reported in the text is based on the entire profile as these samples were collected simultaneously.
Acknowledgements We express our gratitude to the masters and crew of  R/V Pelagia  and  R/V Knorr  for their support during the GEOTRACES expeditions  GA02-PE321  and  GA03-KN204 , respectively. The significant contributions of  Patrick Laan  and  Geoffrey Smith  in expedition preparation are crucial and invaluable. Major contributions have been made by both the Netherlands 2010 and the US 2011 GEOTRACES trace metal teams for shipboard sample collection and filtration. In addition Patrick Laan performed the shipboard dissolved Fe measurements during Pelagia cruise  GA02-PE321  (Rijkenberg et al. , 2014). The authors thank  Matthew Brown  for kindly making available his unpublished dissolved aluminium data from BATS collected and analysed on shipboard during the preceding US GEOTRACES IC1-KN193-5 cruise aboard RV Knorr in June 2008.
9 ± 0. 3 M J and 9. 9 ± 0. 5 M J , respectively) with the hosts being low-mass brown dwarfs (19 ± 1 M J and 26 ± 1 M J , respectively), with very tight orbits (below 0. 4 AU). Similarly,  Han et al. (2013)  report a 23 ± 2 M J field brown dwarf hosting a 1. 9 ± 0. 2 M J planet in a tight system after the analysis of the event OGLE-2012-BLG-0358. The first published microlensing detection of a brown dwarf companion to a star is OGLE-2008-BLG-510/MOA-2008-BLG-369, which was first reported by  Bozza et al. (2012)  as an ambiguous case between a binary-lens and a binary-source event. The data were reanalysed by  Shin et al. (2012a) , who concluded that the binary-lens model involving a massive brown dwarf orbiting an M dwarf was preferred. Shin et al. (2012b)  conducted a database search for brown dwarf companions by focusing on microlensing events that exhibit low mass ratios.
More recently,  Park et al. (2015)  have reported the discovery of a binary system composed of a 33. 5 ± 4. 2 M J brown dwarf orbiting a late-type M dwarf in microlensing event OGLE-2013-BLG-0578. Here we report the first microlensing discovery of a brown dwarf orbiting a Sun-like star. This new brown dwarf has a mass of 42 M J and it was observed at a projected separation of 4. 3 AU from its G-K dwarf host star. In sec. 2, we present photometric data collected on MOA-2007-BLG-197L by several round-the-world telescopes, as well as high resolution adaptative optics images taken with NaCo at VLT. In sec. 3, we present the full analysis of the light curve and in sec. 4 we derive the physical parameters of the lens by combining all independent informations. In sec. 5, we adopt a statistical point of view to analyse the current population of brown dwarfs detected with different methods using non-parametric probability density estimation tools.
MOA On June 19 (THJD 4272), PLANET observers noticed that a few MOA data points were slightly above the standard single-lens theoretical curve. As this usually happens quite often in microlensing data, no alert was released. Indeed SAAO soon revealed that the alleged deviation was a false alert, but followup observations were continued. On July 5 (THJD 4287), even though the full moon affected the quality of the observations, a public alert was issued after Danish 1. 54m data were found to be above the single lens curve by more than 0. 2 mag for more than five consecutive days. Intensive follow-up observations from SAAO confirmed the rise in brightness, announcing a caustic crossing. While Perth was overclouded, Canopus then took over, and was the only telescope to densely cover the caustic exit, which took place during the night of July 4 in Australia , see inset of Fig.
Photometric light curve The photometric data was reduced several times using different software to check their consistency. The final lightcurve data from all telescopes were extracted with the PLANET pipeline PySIS  (Albrow et al. 2009) , which is a DIA-based algorithm (Difference Image Analysis,  Alard 2000; Alard & Lupton 1998). For images taken in particularly bad weather conditions, we examined by eye each image in order to check whether the subtraction was correct or not. In the latter case we had to exclude them, but on the basis of image quality only. In the post-processing of the reduced data, we applied a cut in seeing and sky background, although in a very conservative way so that possible low-amplitude signals were not rejected. Not surprisingly, most of the data taken by MOA during the very bad weather period mentioned before  had to be discarded.
They are summerised in Table  1. As seen in the table, all telescopes use a similar I-band filter, apart from the MOA 1. 8m telescope which is equipped with a broad R/I filter (referred to as R M ). Additionally, a few V-band images were taken by PLANET and µFUN to produce colour-magnitude diagrams (see  Sect. 4. 1). The last concern about the photometry was the estimation of the error bars of the data. Galactic bulge fields are highly crowded with stars, and during a microlensing event, the flux variation can easily span two order of magnitudes for highmagnification events. These pose severe challenges to manage a good estimation of the error bars. As a matter of fact, in microlensing experiments it is long known that data reduction software usually underestimates error bars. Furthermore, error bars can vary significantly from one data set to another, with the risk that one data set dominates over the others at the modelling stage.
We reduced and calibrated the NaCo images following the general method outlined in  Kubas et al. (2012)  and briefly described below. The night was clear and stable according to the observatory night logs, and the target was observed at low airmass and in good seeing conditions (< 0. 8 ). The data were taken in autojitter mode within a 10 jitter box including the target in order to be able to correct for bad pixels and sky background. The AO correction was done using a K = 11. 17 ± 0. 02 mag star (2MASS 18070464-3156423) at an angular separation of about 5 from the microlensing target. The data were dark subtracted, flat-fielded and co-added with the tools underlying the NaCo pipeline software  (Devillard 1999). To derive the photometry from the reduced data, the first step was to compute the zeropoints for the conversion of instrumental magnitudes to calibrated magnitudes.
Following  Kubas et al. (2012) , we constructed a PSF reference directly from stars in the NaCo frame, using the StarFinder package  (Diolaiti et al. 2000). This software was especially designed for AO images of crowded stellar fields. We found J = 17. 68 ± 0. 06, H = 17. 05 ± 0. 05 and K s = 16. 87 ± 0. 05. The quoted error bars are dominated by the uncertainties in determining the true PSF shape and the scatter of the sky and unresolved background sources. The measurements obtained with StarFinder are summarised in Table  2   The final step consisted in correcting the target for interstellar extinction, by fitting the position of the red clump giants (RCG) in the three colour-magnitude diagrams (CMD) involving the measured J, H, K s reddened magnitudes. Because the nominal range in magnitudes from NaCo is above the 2MASS faint limit, we performed InfraRed Survey Facility (IRSF) observations at SAAO to extend the available 2MASS star list into the regime of stars measured within the NaCo frame.
Acknowledgements. A. C. acknowledges financial support from the  Emergence   UPMC 2012  grant. C. R. and A. C. are grateful to  Bo Ma  for providing us with the brown dwarfs catalogue used in Ma & Ge (2014). We especially thank the  University of Tasmania  for granting us access to their TPAC supercomputer facilities where part of the calculations were carried out. Work by C. H. was supported by  Creative Research Initiative Program  ( 2009-0081561 ) of  National Research Foundation of Korea. K. H. , M. D. and M. H. are supported by  NPRP  grant  NPRP-09-476-1-78  from the  Qatar National Research Fund  (a member of  Qatar Foundation ). M. H. acknowledges support from the  Villum foundation. S. D. is supported by "the  Strategic Priority Research Program -The  Emergence of Cosmological Structures" of the Chinese Academy of Sciences  (grant No.
We developed the PHIV-RootCell software to quantify anatomical traits of rice roots transverse section images. Combined with an efficient root sample processing method for image acquisition, this program permits supervised measurements of areas (those of whole root section, stele, cortex, and central metaxylem vessels), number of cell layers and number of cells per cell layer. The PHIV-RootCell toolset runs under ImageJ, an independent operating system that has a license-free status. To demonstrate the usefulness of PHIV-RootCell, we conducted a genetic diversity study and an analysis of salt stress responses of root anatomical parameters in rice (Oryza sativa L. ). Using 16 cultivars, we showed that we could discriminate between some of the varieties even at the 6 day-olds stage, and that tropical japonica varieties had larger root sections due to an increase in cell number.
They are often highly specialized for specific biological samples, and they are not easily adaptable to different issues and/or are not freely available  (Carpenter et al. , 2006; Burton et al. , 2012; Federici et al. , 2012; Pound et al. , 2012). The Java-based ImageJ package 1 offers attractive features such as its license-free status, its operating system independence and its large user community  (Schneider et al. , 2012). Taking advantage of all the tools already available in ImageJ, we developed a new toolset called PHIV-RootCell. 1 http://imagej. nih. gov/ij/ The PHIV-RootCell program is dedicated to the analysis of several root anatomical parameters based on images of rice root transverse sections. The program tracks root tissues and cell walls to measure areas and numbers of cells in cell files.
SPECIFICATIONS, REQUIREMENTS, AND INSTALLATION OF THE PHIV-ROOTCELL TOOLSET The PHIV-RootCell program is a macro toolset running on the 1. 48 h (or higher) version of ImageJ 2. The "Polar Transformer" plugin is needed 3  and has to be saved in the plugins folder (ImageJ/plugins/). All the files related to PHIV-RootCell [user manual, toolset (text file) and examples] are provided as supplementary files. The text file entitled "PHIV_Rootcell_toolset. txt" has to be saved in the ImageJ/macros/toolsets folder. When starting ImageJ, the plugin becomes accessible in the "More Tools" menu ( >> ). When the "PHIV_Rootcell_toolset" is selected, nine new tool buttons appear in the toolbar. The name of each tool is displayed in the status bar when the mouse is moved over it [Parameters, Root Selection (R), Stele Selection (S), Xylem Selection and Count (X), Cortex Selection (C), Layer and Cell File Counts (L), Data Display (D), New Image (N), and About These Macros (?)].
ROBUSTNESS OF THE PHIV-ROOTCELL TOOLSET To assay the reproducibility of the software and the effect of manual corrections on the results, five users analyzed the same dataset composed of 10 biological replicates of Nipponbare cultivar cross-sections to quantify 11 parameters (Figure  S1 ). Statistical analyses suggest that, for the selected images, there is no significant user effect for any of the traits analyzed except for the number of cells in the exodermis layer. Despite a low coefficient of variation (3. 8%) for this parameter, one of the users overestimated it. Because this value can be extrapolated from a portion of the image, a small difference in the real count of cells will be systematically amplified. However, for this parameter, the difference observed between users only varies in average of four cells out of 61.
Analysis of root parameters of 16 rice varieties We analyzed ten anatomical features on 16 rice varieties belonging to seven different varietal groups (Tables  1  and 2 ;  Glaszmann, 1987). A minimum of four images was analyzed per variety. Significant differences between varieties were recorded for all traits (Figure  S2 ). As expected, there are significant redundancies between areas measured due to allometric growth constraints as seen in the correlation matrix (Table  S2 ). A principal component analysis (PCA) was run on the varietal means for all traits. The two first axes of the PCA explain more than 86% of the variability (Figure  3 ; Figure  S3 ). On the first axis (72% of the data dispersion), two distinct groups are clearly visible. The first group clusters the four tropical japonica varieties with thick roots, and the second group gathers all the other varieties with thinner roots.
The second axis is mostly determined by the presence of an outlier, FR13A, belonging to the boro group. All of the rice cultivars analyzed here have fewer than two central metaxylem vessels on average while FR13A has four. Interestingly, the boro variety analyzed (FR13A) is separated from the aus variety (N22) despite their genetic proximity. Due to the small subset of rice varieties analyzed here, it is impossible to say whether this parameter discriminates between the aus and boro accessions, but it is an interesting area to explore. Our data, however, clearly demonstrate that the PHIV-RootCell program can be used to quantify and describe anatomical parameters for genetic analyses of a larger number of varieties, including QTL and genome-wide association studies (GWASs) analyses.