dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  The Sainsbury Laboratory  for hosting the  Open Ash Dieback Git repository  (oadb. tsl. ac. uk), and  Sophie Janacek  for assistance with project management. The submission of sequencing data was brokered by COPO (http://copo-project. org/). The Ash Dieback research project was funded jointly by the  Biotechnology and Biological Sciences Research Council (BBSRC)  and the  Department of Environment Food and Rural Affairs (Defra)  via the 'Nornex' project ( BBS/E/J/000CA523 ) and  Economic and Social Research Council , the  Forestry Commission , the  Natural Environment Research Council (NERC)  and the  Scottish Government , under the  Tree Health and Plant Biosecurity Initiative  ( BB/L01291X/1 ). The UMR IAM is supported by a grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir" program  ( ANR-11-LABX-0002-01 ,  Laboratory of Excellence ARBRE).
At the same time, the relative importance of different factors in modifying dispersal across species has yet to be examined. Synthetic datasets which go beyond single species case examples and thereby allow us to resolve these issues and enable us to establish general dispersal patterns are unfortunately lacking  (Berg et al. , 2010; Urban et al. , 2016). One approach to resolving this problem would be a meta-analysis of published information. However, this approach, based on many studies that each examine a single factor in one specific species, can 1 misestimate the importance of interactions between factors. Few studies manipulate multiple drivers of dispersal at the same time with precludes us from estimating the relative importance of different dispersal drivers and their interactions.
Data collection Data on dispersal, that is, the number of residents (individuals in the patch of origin at the end of the experiment) and dispersers (individuals that had left their patch of origin and were in the target patch at the end of the experiment) after the dispersal phase in each replicate, were either collected using video recording and analysis  (Pennekamp et al. , 2015)  or by direct observation. Using data from further analyses or literature surveys (specified in the Supporting Information), we collected species specific information for the focal species, resources and predators including: movement, space use, feeding strategy, body size, predator specialization and focal species escape strategies. The latter information was either used directly or in relevant focal species to predator ratios as potential explanatory variables for understanding the modulators of resource and predator impacts on dispersal (see Tab.
Dispersal and data collection After the opening of corridors, we monitored dispersal daily for 15 days. To do so, we captured woodlice in the arrival containers without disturbing the departure containers. At the end of the dispersal assay, we captured all woodlice in departure and arrival container. We recaptured 114 of the 236 released woodlice. We only used recaptured ones in our analysis to prevent confounding dispersal with recapture probability. Protists -Chilomonas sp. , Colpidium sp. , Tetrahymena pyriformis and Desxiostoma sp. Authors Emanuel A. Fronhofer, Frank Pennekamp and Florian Altermatt.
Dispersal and data collection After 1 h acclimation phase and 4 h dispersal we closed the clamps and took 20 s videos in start and target patches using a Leica M205 C stereomicroscope at a 16-fold magnification and a Hamamatsu Orca Flash 4 video camera (imaged volume: 34. 4 µL; height: 0. 5 mm). Population densities of residents and dispersers were obtained using video analysis and the 'BEMOVI' package for the R Language and Environment for Statistical Computing  (Pennekamp et al. , 2015). For specific settings see GitHub (https://github. com/efronhofer/analysis script bemovi).
Data collection Sampling regime: Both patches were sampled after the dispersal phase. 1. 2 mL per patch was removed and stored in 1. 5 mL Eppendorf tubes. To preserve the samples and fixate the algae, lugol was added. Densities were determined using a 1 mL counting chamber. Moreover, a sample of 8 µL from the start patch was taken for video analysis (concentrations were too low to take samples of the target patch for video analysis). For measuring dispersal, cell numbers were acquired using a counting chamber.
Dispersal and data Paramecium were allowed to disperse for 3 h, until the connection between origin and target tubes was blocked. We then took 300 µL samples from the tube of origin and 1 mL samples from the target tube. The number of individuals in these samples were counted under a dissection microscope (20x). From these measurement we extrapolated the total number of individuals in both tubes in a total of 13 mL. The entire experiment was conducted within one day, with maximal intervals of 30 min between blocks. Density measurements were taken immediately after the 3 h of dispersal, in the tube of origin and in the target tube. From these measurements we calculated total population size and the proportion of dispersal. As mentioned above, each selection line was used at its 'natural' density, i.
Dispersal and data collection After the opening of corridors, we monitored dispersal daily for 4 days. To do so, two observers entered and walked quietly for 5 minutes in each cages. When a butterfly was located, the observer recorded the individual number. This procedure is commonly used on this species  (Legrand et al. , 2012; Trochet et al. , 2013; Legrand et al. , 2015)  and we did not observe any individuals leaving the cage during dispersal monitoring. We attributed a disperser status to individuals which moved at least once between cages and a resident status to individuals which never left the departure cage. We retained individuals which were alive after three days and excluded individuals which were never observed as their dispersal status is unknown.
Data collection After the opening of corridors, we monitored dispersal daily for 15 days. To do so, we captured spiders in the arrival containers without disturbing the departure containers. At the end of the dispersal assay, we captured all spiders in departure or arrival container, as well as in the corridor. We were able to recapture only 153 spiders. We only used recaptured ones in our analysis to prevent confounding dispersal with the probability of recapture.
Dispersal and data collection After the opening of corridors, we monitored dispersal daily for days. To do so, two people entered and walked quietly for 5 min in each cages. When a damselfly was located, the observer recorded the individual number. This procedure is commonly used on butterfly  (Legrand et al. , 2012; Trochet et al. , 2013; Legrand et al. , 2015)  and we did not observe any individuals leaving the cage during dispersal monitoring. On the fifth day, we captured all survivors in departure and arrival cages. We attributed a disperser status to individuals which moved at least once between cages and a resident status to individuals which never left the departure cage. We retained individuals which were alive after three days and excluded individuals which were never observed as their dispersal status is unknown.
Acknowledgements FA thanks the  Swiss National Science Foundation  (Grant No. PP00P3 150698 ). DB and SM thank the  FWO (Fonds Wetenschappelijk Onderzoek  -grant n. 11T7518N LV). SJ, EL and NS thank  UCL and F. R. S. -FNRS ; SJ acknowledges a " Move-In Louvain" postdoc position at UCL ;  NS is Research Associate of F. R. S. -FNRS. DL and  MD thank Fyssen Foundation  for funding. JDR thanks the  FWO (Research Foundation Flanders  -grant n. FWO14/ASP/075 ). FP was financially supported by  Swiss National Science Foundation  Grant  31003A 159498. JCo was supported by an  ANR-12-JSV7-0004-01 , by the  ERA-Net BiodivERsA , with the national funder ONEMA, part of the 2012-2013 BiodivERsA call for research proposals and by the  French Laboratory of Excellence  project " TULIP"  ( ANR-10-LABX-41 ).
Optical observations The videos used in this study are recorded with a camera installed at Pic du Midi (42. 93N; 0. 14E; 2877 m), as indicated in Figure  1. This camera can be oriented to the storm with a pan-tilt unit that can be remotely controlled via the Internet. The camera is a low-light Watec 902H (minimum illumination of 0. 0001 lux) with a field of view (FOV) of 31° and a highresolution charge-coupled device (CCD). It operates in a triggered mode provided by the UFOCaptureV2 software (http://sonotaco. com/soft/e_index. html#ufocv2, accessed date) to capture luminous events with brightness above a given threshold. The video imagery obtained has a time resolution of 25 frames (or 50 interlaced fields) per second, which corresponds to a time resolution of 20 milliseconds. The images of the videos are de-interlaced for the analysis.
The video camera also records GPS-referenced time and inserts the time in each video frame. Table  1  displays the terms we use in the paper in order to specify the sprite activity at different scales of time. Event specifies sprite occurrence at the scale of a video that lasts 1 or 2 seconds. Some videos included several groups of sprite elements, thus we use sequence for successive sprite fields following a same lightning stroke (as in  Lu et al. [2013] ) and series for a group of sequences that are included in the same video and that are associated to successive strokes that belong (or not) to the same flash. The azimuth and elevation of the sprite events are determined with the software "Cartes du Ciel" (SkyCharts) as described in van der Velde  [2008]. The principle of the software is to overlay known stars on an image from the video imagery, stating the date, time and place of observation.
Cloud structure and characteristics We use the Cloud Top Temperatures (CTT) provided by the Meteosat satellite from the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) is based on radiometer data in the thermal infrared band (IR) at ~11-13 µm. The temperature accuracy is generally better than ~1°. We take into account the parallax error (estimated to be about 15 km for a cloud top at ~12 km at this latitude) for the Figures that associate the locations of CG strokes with their parent clouds. Observations performed by an AEMET network C-band (5 GHz) radar are used in the study. The radar is located in the Balearic Island of Mallorca (39. 379ºN; 2. 785ºE; emitter altitude ~111 m above mean sea level) and it is operated with a 10-minute cycle producing polar volumes.
Thus, the current produced can be due to lightning processes that sufficiently discharges the cloud to produce new sprite elements, especially as VHF sources are detected by the XDDE system along the line of sight of F5 as indicated in Figure  4b, c. It could be also produced by the sprite itself, but it seems more consistent with a current waveform due to M-components superimposed to a long continuing current, as observed by  Lu et al. [2013]  for a dancing sprite. This observation is quite consistent with the study by  Li et al. [2008]  about delayed sprites. Their data showed that 46% of the sprite elements were triggered with a delay larger than 10 ms after the SP+CG strokes and associated with a substantial continuing current. Furthermore, these authors found that an intensification of the continuing current can play a major role in the sprite triggering and that the sferic burst simultaneously recorded is another consequence of the current intensification.
Conclusion We consider data relative to different aspects of the activity of a storm that produced a large number of sprite events, especially to analyze two series of sprite sequences that strongly resemble to dancing sprites. The dataset includes optical emissions recorded by a low-light video camera, characteristics of the CG strokes (time, location, peak current, CMC calculated from ELF signals and current moment waveform), VHF radiations relative to the lightning flashes, electric field radiated by lightning flashes in a large band of low frequencies, CMC calculated from ELF signals, current moment waveform, CTT issued from the radiometer onboard the MSG satellite, and fields of the maximum radar reflectivity. The storm starts at the end of the morning over Mediterranean Sea close to northeastern Spanish coastline, then moves eastward during its lifetime estimated at ~18 hours.
Observations and studies about TLEs made by SS and JFG were partly sponsored by the  National Institute of Universe Science (INSU)  thanks to LEFE/IMAGO. The work of MF is sponsored by the  Natural Environment Research Council (NERC)  under grants  NE/L012669/1  and  NE/H024921/1. JM has been supported by the  National Science Centre  under grant  2015/19/B/ST10/01055. The contribution of Spanish participants to the study was supported by research grants from the  Spanish Ministry of Economy and Competitiveness (MINECO)  and  European Regional Development Fund (ERDF) :  AYA2011-29936-C05-04 ,  ESP2013-48032-C5-3-R  and  ESP2015-69909-C5-5-R. All data used in this paper can be requested from the corresponding author at serge. soula@aero. obs-mip. fr.
During acquisition, the pass energy was set to 100 eV for high-resolution and 500 eV for wide scan spectra. During the processing of the XPS spectra, energy values were referenced to the C 1s peak of adventitious carbon located at 284. 6 eV. CASAXPS software (Casa Software Ltd, Teignmouth, UK, www. casaxps. com) was used for acquisition and data analysis. A Shirley-type background was subtracted from the signals. Recorded spectra were always fitted using GausseLorentz curves, in order to determine the binding energy of the different element core levels more accurately. The error in binding energy was estimated to be ca. 0. 1 eV. Solid-state 13 C CP/MAS NMR spectra of the carbonized resins were recorded at room temperature on a Bruker UltraShield Avance 400 spectrometer at 100 MHz. The CPMAS 13 C NMR experiments were run using a Bruker 4 mm double-resonance probe head at a spinning speed of 12 kHz.
Further information about the composition of the carbonized samples was obtained by XPS. The atomic concentrations of the surface elements are also provided in Table  1 , and the relative distributions of the different species obtained by deconvolution of the C1s and O1s core level peaks of XPS spectra are shown in Fig. 3  (raw data and fitting of all the samples can be found in Fig. S5  and Table  S1 ). The amount of surface oxygen follows the trend: GA < DN < BP < PG, and the trend is similar to that obtained from elemental analysis (accounting for the oxygen content in the bulk). The results indicate a higher retention of oxygen in the samples prepared from the carbonization of GA and PG, which seems reasonable considering that both organic precursors present higher oxygen groups that are expected to be incorporated to the carbon matrix during the cross-linking reactions and/or carbonization.
The largest effect sizes were observed for four rare missense variants, located in the androgen receptor gene AR (rs137852591, MAF=0. 21%, P combined =2. 7×10  -14  ), in CRISPLD2 (rs148934412, MAF=0. 08%, P combined =2. 4×10  -20  ), in IHH (rs142036701, MAF=0. 08%, P combined =1. 9×10  -23  ), and in STC2 (rs148833559, MAF=0. 1%, P combined =1. 2×10  -30  ). Carriers of the rare STC2 missense variant are ∼2. 1 cm taller than non-carriers, whereas carriers of the remaining three variants (or hemizygous men that carry the X-linked AR-rs137852591 rare allele) are ∼2 cm shorter than non-carriers. In comparison, the mean effect size of common height alleles is ten times smaller in the same dataset. Across all 83 rare and low-frequency non-synonymous variants, the minor alleles were evenly distributed between height-increasing and -decreasing effects (48% vs.
Coding variants in new and known height loci, and heritability explained Many of the height-associated variants in this ExomeChip effort are located near common variants previously associated with height. Of the 83 rare and low-frequency nonsynonymous variants, two low-frequency missense variants were previously identified (in CYTL1 and IL11)  3, 14  and 47 fell within 1 Mb of a known height signal; the remaining 34 define new loci. We used conditional analysis in the UK Biobank dataset and confirmed that 38 of these 47 variants were independent from the previously described height SNPs (Supplementary Table  12 ). We validated the UK Biobank conditional results using an orthogonal imputation-based methodology implemented in the full discovery set (Extended Data Fig. 4  and Supplementary Table  12 ).
In addition, we found a further 85 common variants and one low-frequency synonymous variant (in ACHE) that define novel loci (Supplementary Table  12 ). Thus, our study identified a total of 120 new height loci (Supplementary Table  11 ). We used the UK Biobank dataset to estimate the contribution of the new height variants to heritability, which is h 2 ∼80% for adult height  2. In combination, the 83 rare and lowfrequency variants explained 1. 7% of the heritability of height. The newly identified novel common variants accounted for another 2. 4%, and all independent variants, known and novel together explained 27. 4% of heritability. By comparison, the 697 known height SNPs explain 23. 3% of height heritability in the same dataset (vs. 4. 1% by the new height variants identified in this ExomeChip study).
Gene-based association results To increase power to find rare or low-frequency coding variants associated with height, we performed gene-based analyses (Methods and Supplementary Tables  13 14 15 ). After accounting for gene-based signals explained by a single variant driving the association statistics, we identified ten genes with P<5×10 -7 that harbor more than one coding variant independently associated with height variation (Supplementary Tables  16 17 ). These genebased results remained significant after conditioning on genotypes at nearby common height-associated variants present on the ExomeChip (Table  1 ). Using the same gene-based tests in an independent dataset of 59,804 individuals genotyped on the same exome array, we replicated three genes at P<0. 05 (Table  1 ). Further evidence for replication in these genes was seen at the level of single variants (Supplementary Table  18 ).
6 ). The two significant gene sets identified by DEPICT and PASCAL that uniquely implicated coding variants were "BCAN protein protein interaction subnetwork" and "proteoglycan binding. " Both of these pathways relate to the biology of proteoglycans, which are proteins (such as aggrecan) that contain glycosaminoglycans (such as chrondroitin sulfate) and that have wellestablished connections to skeletal growth  17. We also examined which height-associated genes identified by ExomeChip analyses were driving enrichment of pathways such as proteoglycan binding. Using unsupervised clustering analysis, we observed that a cluster of 15 height-associated genes is strongly implicated in a group of correlated pathways that include biology related to proteoglycans/ glycosaminoglycans (Fig. 2  and Extended Data Fig.
Additional interesting height candidate genes include NOX4, ADAMTS3 and ADAMTS6, PTH1R, and IL11RA (Extended Data Tables  1 2 , Supplementary Tables  17  and 24 ). NOX4, identified through gene-based testing, encodes NADPH oxidase 4, an enzyme that produces reactive oxygen species, a biological pathway not previously implicated in human growth. Nox4 -/-mice display higher bone density and reduced numbers of osteoclasts, a cell type essential for bone repair, maintenance, and remodelling12. We also found rare coding variants in ADAMTS3 and ADAMTS6, genes that encode metalloproteinases that belong to the same family than several other human growth syndromic genes (e. g. ADAMTS2, ADAMTS10, ADAMTSL2). Moreover, we discovered a rare missense variant in PTH1R that encodes a receptor of the parathyroid hormone (PTH): PTH-PTH1R signaling is important for bone resorption and mutations in PTH1R cause chondrodysplasia in humans  26.
Genotype calling The majority of studies followed a standardized protocol and performed genotype calling using the designated manufacturer software, which was then followed by zCall  31. For 10 studies participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, the raw intensity data for the samples from seven genotyping centers were assembled into a single project for joint calling  11. Study-specific quality control (QC) measures of the genotyped variants was implemented before association analysis (Supplementary Tables  1 2 ).
Study-level statistical analyses Individual cohorts were analyzed separately for each ancestry population, with either RAREMETALWORKER (http://genome. sph. umich. edu/wiki/RAREMETALWORKER) or RVTEST (http://zhanxw. github. io/rvtests/), to associate inverse normal transformed height data with genotype data taking potential cryptic relatedness (kinship matrix) into account in a linear mixed model. These software are designed to perform score-statistics based rarevariant association analysis, can accommodate both unrelated and related individuals, and provide single-variant results and variance-covariance matrix. The covariance matrix captures linkage disequilibrium (LD) relationships between markers within 1 Mb, which is used for gene-level meta-analyses and conditional analyses  32. Single-variant analyses were performed for both additive and recessive models (for the alternate allele).
2 and 2. 7 for all and common markers, respectively (Extended Data Fig. 2  and Supplementary Table  8 ). Such inflation is expected for a highly polygenic trait like height, and is consistent with our very large sample size  3, 34. To confirm this, we applied the recently developed linkage disequilibrium (LD) score regression method to our height ExomeChip results  30  , with the caveats that the method was developed (and tested) with >200,000 common markers available. We restricted our analyses to 15,848 common variants (MAF ≥5%) from the European-ancestry meta-analysis, and matched them to pre-computed LD scores for the European reference dataset  30. The intercept of the regression of the χ 2 statistics from the height meta-analysis on the LD score estimate the inflation in the mean χ 2 due to confounding bias, such as cryptic relatedness or population stratification.
We found very concordant effect sizes, suggesting that smaller studies do not bias our results (Extended Data Fig. 2 ). Conditional analyses-The RAREMETAL R-package  35  and the GCTA v1. 24  36  software were used to identify independent height association signals across the European descent meta-analysis results. RAREMETAL performs conditional analyses by using covariance matrices in order to distinguish true signals from those driven by LD at adjacent known variants. First, we identified the lead variants (P<2×10 -7 ) based on a 1 Mb window centered on the most significantly associated variant and performed LD pruning (r 2 <0. 3) to avoid downstream problems in the conditional analyses due to co-linearity. We then conditioned on the LD-pruned set of lead variants in RAREMETAL and kept new lead signals at P<2×10 -7.
The process was repeated until no additional signal emerged below the prespecified P-value threshold. The use of a 1 Mb window in RAREMETAL can obscure dependence between conditional signals in adjacent intervals in regions of extended LD. To detect such instances, we performed joint analyses using GCTA with the ARIC and UK ExomeChip reference panels, both of which comprise >10,000 individuals of European descent. With the exception of a handful of variants in a few genomic regions with extended LD (e. g. the HLA region on chromosome 6), the two software identified the same independent signals (at P<2×10  -7  ). To discover new height variants, we conditioned the height variants found in our ExomeChip study on the previously published GWAS height variants 3 using the first release of the UK Biobank imputed dataset and regression methodology implemented in BOLT-LMM  37.