dataesr/scientific-paragraphs-categorization huggingface dataset:
Loftus  and  W. Pavan  from  National Human Genome Research Institute  for the help with melanocyte eQTL study. This work has been supported by the  Intramural Research Program (IRP) of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, US National Institutes of Health. The content of this publication does not necessarily reflect the views or policies of the  US Department of Health and Human Services , nor does mention of trade names, commercial products, or organizations imply endorsement by the  US government. The Vermeulen lab is part of the  Oncode Institute , which is partly funded by the  Dutch Cancer Society (KWF).
Data availability The data generated during the current study are deposited in Gene Expression Omnibus (https://www. ncbi. nlm. nih. gov/geo/) as a SuperSeries under the accession number GSE129250. A complete list of oligo sequences for MPRA libraries and complete processed MPRA data can be found in Supplementary File 1 as an R object. Melanocyte eQTL data and expression data from 106 individuals are available through the database of Genotypes and Phenotypes (dbGAP, https://www. ncbi. nlm. nih. gov/gap) under accession number phs001500. v1. p1.
URLs ENCODE Project, https://www. encodeproject. org/; Roadmap Epigenomics Project, http://www. roadmapepigenomics. org/; UCSC Genome Browser, http://genome. ucsc. edu/; GTEx Portal, http://www. gtexportal. org/home/testyourown; TCGA, https://cancergenome. nih. gov/; motifbreakR, http://bioconductor. org/packages/motifbreakR/; PAINTOR, https://github. com/gkichaev/PAINTOR_V3. 0; 3D Genome Browser, http://promoter. bx. psu. edu/hi-c/; LeafCutter, https://davidaknowles. github. io/leafcutter/articles/sQTL. html; TIMER, https://cistrome. shinyapps. io/timer/; CIBERSORT, https://cibersort. stanford. edu/; minfi: http://bioconductor. org/packages/minfi/; REMBRANDTS: https://github. com/csglab/REMBRANDTS made available for use under a CC0 license.
3. 3 made available for use under a CC0 license. 20   Variant is located within a human melanocyte H3K27Ac ChIP-Seq peak from one or more individuals AND a H3K4Me3 ChIP-Seq peak from one or more individuals of two and three available through Epigenome Roadmap database, respectively. 4. Variant is located within a human melanoma short-term culture FAIRE-Seq peak from one or more individuals of 11 available from Verfaillie et al 26.
1 mm, and projected onto the surface (0-1. 5 mm from the organ surface). Note that the cell wall thickness at the L1/L2 interface of less than 200 nm is below the z-resolution limit of CLSM, we therefore cannot exclude a contribution of microtubules from the outer face of cortex cells to our measurements at the L1/L2 interface, and therefore use the term 'L1/L2 interface' instead of inner periclinal face. To quantify root thickness, bright-field images of lateral roots were imported into Fiji, both sides of the root were traced manually along their longitudinal axis, and XY Cartesian coordinates for each pixel on the outline trace were exported as csv files and imported into RStudio (https://www. rstudio. com/). For each pixel on one side, the closest neighbour on the other side was determined and the Euclidian distance between pixels calculated using the nn2 function in the RANN package (https:// CRAN.
Statistical data analysis and plotting Two-way ANOVA (analysis of variance) was performed in R using the aov function from the stats package (https://stat. ethz. ch/R-manual/R-devel/library/stats/), Tukey's test was performed in R using the TukeyHSD function from the stats package, Student's t-test and, for samples with unequal variance, Welch's t-test were performed in R using the t. test function from the stats package. Plots were generated in R using the plot function from the graphics package (R Core Team, 2012; https://stat. ethz. ch/R-manual/R-devel/library/graphics/), in Gnumeric (http://www. gnumeric. org/), or in Microsoft Office Excel 2016 (https://products. office. com/en-gb/excel). A o ¼ I þ 1 À 3 ð Þa o a o (7) The value adopted by the dispersion parameter x determines the degree of anisotropy.
Acknowledgements The work on this manuscript has been overshadowed by the untimely death of Ian Moore in August 2018. In sorrow, we dedicate this work to his memory. We thank  Geoff Wasteneys  for providing the CLASP:GFP and RFP: TUB6  lines,  Takashi Hashimoto  for the spr3 and GCP2:GFP lines, and  Jane Langdale  for critical reading of the manuscript. This work was supported by  Leverhulme Trust  grant  RPG-2014-276  and a  John Fell Award  to IM,  BBSRC  grant  BB/P01979X/1  to IM, CK, and AJ, and a  BBSRC  doctoral training award,  Clarendon Scholarship , and  Leverhulme Early Career Fellowship   ECF-2017-483  to CK. DG-G and AJ acknowledge funding from the  European Union 's  Seventh Framework Programme  ( FP7 2007-2013 )  ERC Grant  Agreement No. 306587.
We provide an assessment of the degree of completeness with respect to the parent GRB population, in terms of the X-ray properties of the bursts in the sample and find that the sample presented here is representative of the full Swift sample. We constrain the fraction of dark bursts to be < 28 per cent and we confirm previous results that higher optical darkness is correlated with increased X-ray absorption. For the 42 bursts for which it is possible, we provide a measurement of the neutral hydrogen column density, increasing the total number of published HI column density measurements by ∼ 33 per cent. This dataset provides a unique resource to study the ISM across cosmic time, from the local progenitor surroundings to the intervening universe.
We here make all the data resulting from the survey publicly available in reduced form (see Sect 3. 7). The paper is organized in the following way: In Sect. 2 we describe the sample including the sample selection criteria and the observational strategy. In Sect. 2. 4, we describe the observations and the instrumental setups, and in Sect. 3 we detail the methodological strategies adopted in the data reduction process and auxiliary material. In Sect. 4 we describe the results of the survey, i. e. the efficiency of the follow-up effort and the characteristics of the observed bursts. We also assess the completeness of the realized sample. Finally, we offer our conclusions in Sect. 5. We use the ΛCDM cosmology parameters provided by the Planck  Collaboration et al. (2016)  in which the universe is flat with H 0 = 67.
If this is not possible, we use the built-in acquisition camera of Xshooter to search for counterparts, and adapt the observing strategy on the fly. In many cases we could interact directly with the staff at the telescope to aid the observation. Raw data are usually available within minutes in the ESO archive and are promptly reduced by members of our collaboration, often using archival calibration data which are readily available. This allows us in most cases to report the preliminary results (redshift, identification of the most prominent emission and absorption feature, etc. ) within a few hours after the beginning of the observations and plan additional X-shooter observations if deemed necessary.
Data processing In this section we describe how the final data products are produced and subsequently post-processed. All postprocessing scripts developed for this dataset are made publicly available at https://github. com/jselsing/XSGRB_ reduction_scripts. Before any reductions are initiated, the raw object images are run through the cosmic-ray removal algorithm (van Dokkum 2001) implementation, Astro-SCRAPPY 5  , where a wide clipping radius was used around detected cosmic ray hits to ensure that edge residuals are robustly rejected. The basis for the reductions is the VLT/X-shooter pipeline, version 2. 7. 1 or newer  (Goldoni et al. 2006; Modigliani et al. 2010 ). The pipeline is managed with the Reflex interface  (Freudling et al. 2013)  and is used for subtraction of bias level, flat-fielding, tracing of the echelle orders, wavelength calibrations with the use of arc-line lamps, flux calibration using spectrophotometric standards  (Vernet et al.
Spectral resolution The afterglow spectra described in this paper are obtained in Target-of-Opportunity mode. In most cases, there is little possibility to tweak slit widths to the seeing at the time of observations (i. e. to optimize spectral resolution and signal-to-noise), and almost all our data are therefore taken with a fixed set of slit widths and binning, described above. In a fair number of cases, the seeing full width at half maximum (FWHM) is considerably smaller than the slit width, and the delivered spectral resolution will then be determined by the seeing rather than by the slit width, as afterglows are point sources (this is evidently not the case for extended sources, e. g for host galaxies). The delivered resolution for slit-width dominated spectra post-reduction and extraction can easily be determined from the bright sky emission lines.
2. Blue data points show the FWHM (km/s) of Gaussian fits to unresolved telluric absorption lines in the VIS spectra, as a function of the airmass corrected DIMM seeing. The red data points show a subsample of measurements obtained for NIR spectra. The colored lines show the corresponding linear fits to the data points. The colored bounds contain 68 per cent of the best-fit probability mass. The red and blue dashed lines indicate the nominal resolution for the slit-widths used for the telluric standard observations. As can be seen, the effective resolution is in many cases superior to the nominal one. spectra, usually just after the science data, as part of the ESO X-shooter calibration plan. These spectra have been reduced together with the afterglow spectra, using identical pipeline settings with the same version of the pipeline.
An example of the performance is shown in Fig. 3. In the Lyα forest, where there is very little flux at the continuum level, the performance of the normalization algorithm depends on the continuum coverage redwards of the Lyα line. In some cases, very little continuum is contained in a single arm and a manual continuum estimate is provided, similar to what is done in  López et al. (2016). In these cases the continuum error is set to 10 per cent. The code for the continuum estimate is released along with the paper at https://github. com/jselsing/XSGRB-sample-paper.
Science data products All the spectra are made available as a single ZIP file, through http://grbspec. iaa. es (de Ugarte Postigo et al. 2014a), and additionally through the ESO archive in the form of phase 3 material. This release includes both prompt afterglow observations as well as late time observations of the associated hosts, and represents all afterglow spectra of GRBs carried out by the X-shooter spectrograph since the commissioning of the instrument, 14/03/2009, and until the end of the last period of the program 098. A-0055, 31/03/2017 and thus constitutes eight years of GRB afterglow observations with X-shooter. An overview of all the spectra and their observational setups is given in Table  1. For each burst, each individual observation is provided in a separate reduction, and in cases where observations have been repeated for an increased signal-to-noise or to follow the temporal evolution, a combined spectrum is also provided.
3 -10 keV) at 11 hours, and the intrinsic X-ray derived equivalent hydrogen column density at the redshift of the GRB, in excess of the Galactic X-ray absorption column, N HI,X , in Fig. 5. For the latter, we can only use values of N HI,X derived for bursts with a measured redshift, excluding ∼ 75 per cent of the full Swift sample. We return to the last point in Sect. 4. 4. Using the observational characteristics of the 1266 bursts observed until 31/03/2017 by Swift, and the derived N HI,X  (Evans et al. 2009) , we can quantify the degree to which our sample is biased relative to the overall Swift sample. The values are queried from the online Swift database 10,11. Three samples are of interest in order to assess the completeness of the follow-up campaign (Fig. 5 ); the full Swift sample consisting of all the bursts observed by Swift (blue), all the bursts that fulfill the selection criteria imposed in Sect.
Acknowledgements. Much of the work done here would not have been done without  Neil Gehrels , who has now passed away. We owe him a large debt for his invaluable work with the Swift satellite. We also want to remember  Javier Gorosabel , who was taken from us too early. JPUF, BMJ and DX acknowledge support from the  ERC-StG  grant  EGGS-278202. The  Dark Cosmology Centre  was funded by the  Danish National Research Foundation. This work was supported by a  VILLUM FONDEN   Investigator grant  to  JH  (project number  16599 ). TK acknowledges support by the  European Commission  under the  Marie Curie Intra-European Fellowship Programme  in FP7. LK and JJ acknowledges sup-.
port from  Juan de la Cierva Incorporación  fellowships IJCI-2015-26153. RSR acknowledges  AdUP's BBVA Foundation  Grant for Researchers and Cultural Creators and support from  ASI (Italian Space Agency)  through the Contract n. 2015-046-R. 0 and from  European Union   Horizon 2020 Programme  under the  AHEAD  project (grant agreement n. 654215). GL is supported by a research grant ( 19054 ) from VILLUM FONDEN. SDV acknowledges the support of the  French National Research Agency (ANR)  under contract  ANR-16-CE31-0003  BEaPro DM acknowledges support from the  Instrument Center for Danish Astrophysics (IDA). This work made use of data supplied by the  UK Swift Science Data Centre at the University of Leicester. Finally, it is with pleasure that we acknowledge expert support from the  ESO  staff at the  Paranal  and  La Silla  observatories in obtaining these target of opportunity data.
AG acknowledges the financial support from the Slovenian Research Agency (research core funding No. P1-0031 and project grant No. J1-8136). CT acknowledges support from a Spanish National Research Grant of Excellence under project AYA 2014-58381-P and funding associated to a Ramón y Cajál fellowship under grant number RyC-2012-09984. AdUP acknowledges support from a Ramón y Cajal fellowship, a BBVA Foundation Grant for Researchers and Cultural Creators, and the Spanish Ministry of Econ- omy and Competitiveness through project AYA2014-58381-P. ZC acknowledges support from the Spanish research project AYA 2014-58381-P and support from Juan de la Cierva Incorporación fellowships IJCI-2014-21669. DAK acknowl- edges support from the Spanish research project AYA 2014-58381-P and sup- Article number, page 15 of 43.
(b) Cloning of R. antilleana Scr and Ubx Extraction of total RNA from a mix of 10 complete R. antilleana first to fifth instar nymphs was performed using Trizol. cDNA was synthesized using SuperScript III First-Strand kit according to the manufacturer's instructions (Invitrogen). Fragments of Scr and Ubx genes were amplified (electronic supplementary material, figures S1 and S2) by PCR using GoTaq G2 DNA polymerase (Promega) and the primers described in electronic supplementary material, table  S1. PCR products were examined by electrophoresis, purified using PCR Minelute kit (Qiagen) and cloned into a pGEM-T vector kit (Promega). The sequences of R. antilleana Scr and Ubx can be retrieved in GenBank using the following accession numbers: MG999826 and MG999808, respectively, and in electronic supplementary material, figures S1 and S2.
(e) Statistical analysis Shapiro tests (to test for the normal distribution of data), Student's t-tests and Wilcoxon tests (depending whether variables followed normal distributions) were performed using RStudio  royalsocietypublishing. org/journal/rsbl Biol. Lett. 15: 20180720 version 1. 0. 153 for the statistical analysis of phenotypic quantification. R script used in this study  [22]  is available in the Dryad Digital Repository using the following link: http://dx. doi. org/ 10. 5061/dryad. s76h0s6. (c) ( d) (g) ( h) (e) ( f ) (i) (k) (l) ( j ).
These interactions and regulations might allow Hox transcription factors to be expressed in different cell populations within the same segment  [16, 32]  and are consistent with sex-specific gene regulation participating in intra-locus sexual conflict  [12, 36]. Testing these hypotheses should further improve our understanding of the developmental genetic basis of antagonistic coevolution and the underlying role of Hox genes and their sex-specific regulation. Data accessibility. Sequences of Rhagovelia antilleana Ultrabithorax and Sex combs reduced can be retrieved in GenBank under the following accession numbers: Ultrabithorax: MG999808; Sex combs reduced: MG999826. Data and R script  [22]  used for the analysis performed in this study are available on the Dryad Digital Repository: http:// dx.
Present-day global land cover descriptions are generated at 1-km resolution using updated versions of the data and methods used for CLM4 and CLM4. 5  (Lawrence & Chase, 2007). The basis for the land cover description comes from MODIS land cover (MCD12Q1 v5. 1), vegetation continuous fields (MOD44B v5. 1), LAI (MCD15A2 v5), and albedo (MCD43B3 v5) products for the years 2001-2015 (https://lpdaac. usgs. gov/dataset_discovery/modis). Additional information for tree leaf type and longevity are provided by the AVHRR continuous fields tree cover product  (Defries et al. , 2000). Global crop distributions are provided by the monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000) data set of  Portmann et al. (2010). Canopy height data for tree PFTs are provided by the Geoscience Laser Altimeter System on the ICESat satellite as processed by  Simard et al.
Rivers The River Transport Model (RTM) used in CLM4. 5 is replaced with the physically more realistic Model for Scale Adaptive River Transport (MOSART,  Li et al. , 2013). Note that the river model is treated as a separate coupled component in CESM and therefore is not technically part of CLM, but we include it in this manuscript because of the clear relationship with and dependence on CLM; that is, MOSART receives surface and subsurface runoff from CLM. MOSART represents an upgrade over RTM in several ways. RTM utilizes a simple linear reservoir method to calculate streamflow, while MOSART is based on the more physically based kinematic wave method. MOSART also provides more information on river conditions; that is, RTM only simulates streamflow whereas MOSART additionally simulates time-varying channel velocities, channel water depth, and channel surface water variations.
Plant Hydraulics Parameters The plant hydraulics scheme introduces four new parameters for each PFT  (Kennedy et al. , 2019) , including the water potential at which half of the hydraulic conductivity of each plant element (root, stem, shaded leaf, and sunlit leaf) is lost (p50), the conductivity of the soil-root interface (krmax), the conductivities at the interfaces between each of the plant elements (kmax), and the cavitation vulnerability curve shape-fitting parameter (ck). The code is structured so that in future investigations, parameter values for each plant element (3) Leaf longevity (leaf_long), target leaf CN ratio (leafcn), and specific leaf area (slatop) were all derived from the mean PFT-specific values identified in the TRY database  (Kattge et al. , 2011). With our final set of default CLM5 parameters, the productivity for boreal and temperate needleleaf evergreen trees is too high, particularly when the LUNA model is active.
To calibrate model performance, leafcn was increased to one standard deviation above the mean reported value for these PFTs. The parameters for carbon allocation are as follows: ratio of new coarse root to new stem allocation, croot_stem; ratio of new fine root to new leaf allocation, froot_leaf; and ratio of new stem to new leaf allocation, stem_leaf. The ratios of tissue biomass are the basis for the fixed carbon allocation scheme used in CLM5, which is an oversimplification of real allometric ratios that vary as plants age. Thus, it is difficult to directly connect the parametric allocation ratios used in CLM5 to those obtained from databases. The CLM5 allocation parameters (ratio of new coarse root to new stem, croot_stem; ratio of new fine root to new leaf, froo-t_leaf; ratio of new stem to new leaf, stem_leaf; and ratio of new live wood to new total wood) were initially derived from an analysis by  Ghimire et al.
(2010) , modular allometry and allocation schemes, flexible trait-based PFT definition, interactive logging, and plant hydrodynamics based on  Christoffersen et al. (2016). FATES fast-timescale physiological processes are based on CLM but resolved for a height-structured and multi-PFT canopy. FATES is not active by default in CLM5 and is not active within any simulations assessed in this manuscript. Open-source development and application of the codebase is ongoing (https://github. com/NGEET/fates).
ILAMB The International Land Model Benchmarking (ILAMBv2. 1, Collier et al. , 2018)  package is used to assess the models. ILAMB is an open-source land model evaluation system that operates on global-, regional-, and sitelevel data and provides a hierarchical scoring system to indicate model fidelity. The ILAMBv2 version used here integrates analysis for 28 variables utilizing more than 60 data sets and data products. For each variable, ILAMB produces statistics, maps, time series, and metrics for annual mean, bias, relative bias, RMSE, seasonal cycle phase, spatial distribution, interannual variability, and variable-to-variable assessments. Both global and regional assessments are included. To address a range of questions related to the impacts of model • structural evolution BGC mode (CLM4BGC, CLM4.
5BGC, CLM5BGC; GSWP3v1 and CRUNCEPv7 forcing); • structural evolution prescribed vegetation mode (CLM4SP, CLM4. 5SP, CLM5SP; GSWP3 forcing); • forcing uncertainty (CLM5BGC and CLM5SP with GSWP3, CRUNCEP, and WATCH/WFDEI); • structural and forcing uncertainty (CLM4, CLM4. 5, CLM5 with GSWP3, and CRUNCEP, SP, and BGC). The CLM diagnostics package provides a vast set of additional plots and tables, including plots for many variables that are not included in ILAMB as well as seasonal comparisons against selected observed data sets. CLM diagnostic package results are available here for reference (http://www. cesm. ucar. edu/experiments/ cesm2. 0/land/diagnostics/clm_diag_PCKG. html).
Water from snow melt and spring rains then infiltrates deeper into the soil column (which is 8. 5 m deep at this location), providing a source of moisture for evaporation into the summer months. At the eastern U. S. grid cell, we can observe a different feature of the new model. The shallow 1-m-thick soil prescribed at this location in CLM5 cannot store much water. Consequently, we can see strong drying throughout the soil column in the low precipitation year of 1993, which then restricts ET from summer into fall, in agreement with observations. In CLM4 and CLM4. 5, ET is unrealistically supported through this period by soil water that is stored deeper in the standard 3. 5m-thick soils. ILAMB and CLM diagnostics package results indicate only relatively small changes in the quality of annual streamflow for the top 50 biggest rivers.
Journal of Advances in Modeling Earth Systems Assessment of the impact of hydrology changes on simulated land-atmosphere interactions is beyond the scope of this manuscript. However, we can infer that the relationship is likely to differ by examining the simulated soil moisture residence time (SMRT) across models. SMRT is the e-folding decay timescale of soil moisture due to evapotranspiration and is an integrative measure of soil-plant-atmosphere dynamics. We calculate SMRT for the root zone (0-0. 5 m) from daily soil moisture curves during post-rain periods using a procedure similar to the estimation of a base flow recession constant  (Vogel & Kroll, 1996). This residence time metric is reflective of the evapotranspiration dry-down response timescale  (Teuling et al. , 2006). In Figure  8 , SMRT as simulated by CLM5 is shown for the continental United States for the May to October warm season and is compared to observationally derived estimates from the North American Soil Moisture Database  (Quiring et al.