dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of the dynamics The dynamic evolution of d-score with time was analyzed for each peak with a d-score calculated on the 10 samples (5 times, 2 replicates). Moreover, only peaks that had a biallelic d-score at time 0 (comprised between 0. 3 and 0. 7) were selected. Then normalized d-scores were calculated by dividing each d-score by the corresponding initial d-score (time 0) and by 2, so that initial normalized d-scores start at 0. 5. Initially we have fitted either exponential or sigmoidal curve to these values. We compared the quality of this fitting by taking into account the number of parameters used in the models (AIC and BIC criteria) and concluded that the sigmoidal model best reflects the observed dynamics (data not shown). Sigmoidal fitting of normalized d-scores in function of time has been done for each peak with a four-parameter log-logistic function from drc R package  (Ritz et al.
Moreover, for all active histone marks, IC25, IC30 and IC40 (thresholds of normalized d-score:0. 25; 0. 3; 0. 4) were also tested and were able to replicate all our major findings (data not shown). Features comparison between early and late silenced peaks Early, intermediate and late silenced peaks located on TSS were defined with a 3 clusters k-means based on IC35 values. Early and late silenced peaks were compared for several features using Wilcoxon test: level of expression of associated genes (from TT-seqsee below), distance to Xist, distance to the closest TAD boundary  (Dixon et al. , 2012) , distance to the closest LAD  (Peric-Hupkes et al. , 2010) , LINE density in a window of 100kb around the peak (from RepeatMasker database) and gene density in a window of 100kb around the peak. Moreover, IC35 was compared for peaks that are inside or close to Xist entry sites  (Engreitz et al.
Counts and normalization It should be noted that a d-score analysis, as used for active mark loss, involves the use of the active allele as an internal control, but in the case of H2AK119Ub and H3K27me3, this was hindered by very low enrichment for repressive marks across the Xa. For all defined windows, total and allelic reads overlapping those features were then counted using featureCounts (1. 5. 1), with options [-C -p -P]  (Liao et al. , 2014). Then, analysis was done based on normalized reads from B6 allele (allele of the inactive X chromosome). For each sample, a normalization factor was calculated with the trimmed mean of M-values method (TMM) from edgeR package  (Robinson et al. , 2010) , based on B6 reads overlapping consensus peaks located on autosomes (identified and defined as consensus such as for active histone marks analysis).
For pairwise comparison between marks, only windows with ED 50 /maximum 2nd derivative inferior to 24h for both marks were selected. For comparison of ED 50 between different genomic subcategories (active promoters, gene bodies, enhancers, intergenic regions) for a same histone mark, no filter was done. To test the stability of our results, main analysis was also performed with different thresholds of residuals (0. 2; 0. 15; 0. 1; 0. 05). Moreover, repressive marks dynamics were also analyzed using allelic ratio (d-scores), such as for analysis of active histone marks, using ED 50 , IC60 and IC65 as criteria. All those analysis revealed stable and reproducible results (data not shown).
25. 0) (Quinlan and Hall, 2010). Those files were then mapped with Tophat (2. 1. 0) (Trapnell et al. , 2009) , with options [-p 8 -g 1 -x 1 -N 3 --read-edit-dist 3 --no-coverage-search --library-type fr-secondstrand], with refFlat annotation. Reads covering exons of each gene were then counted with featureCounts (1. 5. 1) with options [-C -p -s 1 -T 8]  (Liao et al. , 2014). D-scores were then calculated for genes with minimum 10 allelic reads. For differential gene expression analysis prior to DOX treatment, analysis was done on genes for which 2 among the 4 samples have a TPM superior to 1. Normalization was done using TMM  (Robinson et al. , 2010)  method from edgeR and differential analysis using Voom function from Limma R package  (Ritchie et al. , 2015).
nChIP-seq The processing from raw data (fastq) to clean mapped file was done such as for time course nChIP-seq. Then, for active histone marks, the reads counts of H3K27ac and H4ac nChIP-seq were done on consensus H3K27ac peaks and consensus H4ac peaks defined in time course analysis, using featureCounts (1. 5. 1), with options [-C -p -P]  (Liao et al. , 2014). D-score was calculated with a slight modification for one of the 2 mutant sample. Indeed, this sample showed a non biallelic signal on X chromosome in its input, due to the presence of some XO cells. To take this bias in account, a normalization factor was calculated as follow: normFactor = (reads B6 /(reads B6 + reads CAST )) / (1-(reads B6 /(reads B6 + reads CAST ))). Then d-score for this mutant was adjusted from classic d-score as follow: d-score = d-score/(d-score + normFactor -normFactor * d-score).
After this adjustment, only the peaks that were biallelic at time 0 in WT and mutant samples were selected (d-scores between 0. 3 and 0. 7) for analysis. For repressive marks, analysis was done such as for time course nChIP-seq, based on signal accumulation of 10 kb windows all along the X chromosome. For differential peak signal analysis at t = 0hr, normalization was done using TMM  (Robinson et al. , 2010)  method from edgeR and differential analysis using Voom function from Limma R package  (Ritchie et al. , 2015). Comparison of sensitive and resistant peaks (nChIP-seq on active histone marks) / genes (RNA-seq, ChIP-seq on repressive histone marks) Sensitive and resistant peaks/genes were defined as follow. For nChIP-seq on active histone marks and RNA-seq, only peaks/genes with d-scores considered as stable between replicates (diff replicate < median + 2sd of all differences between replicates) for both clones (WT, mutant) at each time point were selected.
D-scores were normalized to time 0 (d-score / d-score t0), then only peaks/ genes considered as not escaping were selected (normalized d-score at t24 < 0. 8). Difference of normalized d-score at 24h between WT and mutant was then calculated, and divided by the difference of normalized WT d-score between 0 and 24h for the WT sample. K-means with 3 clusters was done on this normalized difference, identifying 3 groups: sensitive, intermediate and resistant peaks/ genes. For nChIP-seq on repressive histone marks, differential enrichment between WT and mutant was defined as the difference between WT normalized BL6 enrichment relative to time 0 and Mutant normalized BL6 enrichment relative to time 0, divided by WT normalized BL6 enrichment relative to time 0. Analysis of FLAG ChIP-seq on Hdac3 Flag/Flag ESC cell line The processing from raw data (fastq) to d-score calculation was done such as for time course nChIP-seq, with slight modifications.
For windows and peaks analysis, windows that had less than one read per 50kb for more than 2 among the 8 samples were removed for the analysis. Normalization factors were calculated based on windows located on autosomes (with selection of the 10000 most higher signal for intergenic window analysis), with TMM method using edgeR  (Robinson et al. , 2010). Because of the high variability in proportion of cells involved in X chromosome inactivation quantified by the presence of Xist Cloud by FISH experiments (TXY:XistWT#1 46. 64%, TXY:XistWT#2 59. 44%, TXY:XistDA#1 50. 61%, TXY:XistDA#2 48. 24%), linear regression including the percentage of induction calculated by FISH was fitted for each window according the following model: $0 + clones + clones: induction, using Voom function from Limma R package  (Ritchie et al.
Proteomics data analysis For identification, the data were searched against the Mus musculus (UP000000589) UniProt database using Sequest HF through proteome discoverer (version 2. 2). Enzyme specificity was set to trypsin and a maximum of two-missed cleavage sites were allowed. Oxidized methionine, N-terminal acetylation, and carbamidomethyl cysteine were set as variable modifications. Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0. 6 Da for MS/MS peaks. The resulting files were further processed using myProMS v3. 6  (Poullet et al. , 2007). FDR calculation used Percolator and was set to 1% at the peptide level for the whole study. The label free quantification was performed by peptide Extracted Ion Chromatograms (XICs) computed with MassChroQ version 2.
ACKNOWLEDGMENTS We are grateful to  Magdalena Zernicka-Goetz  and  Elizabeth Blackburn  for their continuing support of this project. We are thankful to  Samuel Collombet ,  Tomasz Chelmicki ,  Pierre Gestraud ,  Aurelie Teissandier ,  Nicolas Servant , and  Emmanuel Barillot  for help with the project and members of the  Heard lab  for helpful discussions. We are grateful to  Anton Wutz  for sharing his TXY ESC line. We also thank the tissue-imaging platform- PICT-IBiSA  ( UMR3215/ U934 ) and the next-generation sequencing platform of  Institut Curie. This work was funded by  ERC  advanced investigator awards (ERC-ADG-2014 671027 to E. H. and  ERC -ADG-2015 693023 to P. C. ),  Labellisation La Ligue ,  ANR  ( DoseX 2017 :  ANR-17-CE12-0029 ,  Labex DEEP :  ANR-11-LBX-0044 ,  ABS4NGS :  ANR-11-BINF-0001 , and part of the  IDEX PSL :  ANR-10-IDEX-0001-02 PSL  to E.
New information We here provide a consistent dataset synthetising all eDNA sampling sites in French Guiana to date. Field collections have been initiated in 2014 and have continued until 2019. This dataset is however a work in progress and will be updated after each collecting campaign. We also provide a taxon by site matrix for fishes presence / absence as inferred from eDNA. Our aim is to allow a transparent communication to the stakeholders and provide the foundation for a monitoring programme based on eDNA.
2014 , Melki 2016). As a consequence, collecting data on entire assemblages is almost impossible using traditional sampling methods, which act as a barrier to scientific advances on ecosystem structure and function and associated applied issues on biodiversity conservation and management. Since 2014, we used a non-destructive alternative to traditional fish sampling by characterising species assemblages using environmental DNA [hereafter eDNA] metabarcoding  (Taberlet et al. 2018 , Taberlet et al. 2012). eDNA consists of collecting DNA released by organisms directly into the water. Environmental DNA sequences are then compared to reference molecular databases to assign sequences to species. This method has been shown to efficiently characterise fish faunas in temperate rivers  (Civade et al.
2016 , Valentini et al. 2016)  and has recently been successfully applied in French Guiana  (Cilleros et al. 2019 , Cantera et al. 2019). We here provide a consistent dataset synthetising all eDNA sampling sites in French Guiana to date. We also provide a taxon by site presence/absence matrix for the fish fauna. Our aim is to allow a transparent communication to the stakeholders and provide the foundation for a monitoring programme based on eDNA.
Study area description: Collecting trips have been conducted in various locations throughout French Guiana. Design description: This dataset was developed to provide the foundation for a biodiversity monitoring programme based on eDNA but also to better understand the impact of human activities on aquatic biodiversity. Locations were thus selected to maximise the geographic coverage of rivers and streams, taking into account undisturbed sites but also sites under human disturbances (close to villages, close to gold mining sites etc. ). Funding: Data for this resource have been obtained with support from Labex CEBA (Center for the Study of Biodiversity in Amazonia), Labex DRIIHM (Dispositif de Recherche Interdisciplinaire sur les Interactions Hommes-Milieux) and Labex TULIP (Towards a Unified theory of biotic interactions: role of environmental perturbations).
Labex (Laboratoires d'Excellence) are funded by "Investissement d'Avenir" grants managed by the French National Research Agency (ANR) under references ANR-10-LABX-25-CEBA, ANR-11-LABX-0010-DRIIHM and ANR-10-LABX-0041-TULIP. Additional financial support was also obtained from the DEAL Guyane, Office de l'Eau Guyane (Aquatic Metabarcoding project) and through the ANR DEBIT project (ANR-17-CE02-0007-01). SPYGEN, a private company specialised in eDNA, as well as VigiLife, a non-governmental agency, provided financial and laboratory support. Logistic support was also provided by the Parc Amazonien de Guyane and Hydreco Laboratory (Kourou, Guyane).
Taxonomic coverage Description: The dataset provides information on eDNA sampling sites and fishes presence/absence as inferred from metabarcoding analyses  (Cilleros et al. 2019). DNA extracted from the sampling cartridge could, in theory, be used for amplifying any taxonomic group, depending on the downstream molecular biology protocols. Local metabarcoding reference databases for French Guiana biodiversity are currently available for mammals  (Kocher et al. 2017b , Kocher et al. 2017a ) and insects  (Talaga et al. 2017 , Kocher et al. 2016) , but additional databases are under active development for other groups as well.
Data resources Data package title: Aquatic eDNA for monitoring French Guiana biodiversity Resource link: http://vmcebagn-dev. ird. fr/geonetwork/srv/eng/search?=eng#|5617a9ff-d0aa-48a9-b2c2-cb7fd5b92692 Alternative identifiers: 5617a9ff-d0aa-48a9-b2c2-cb7fd5b92692.
Width Watercourse width (in metres) measured at the sampling site. Data format: Spreadsheet in "tab separated value" Description: This dataset provides a taxon by site matrix, made after sequences assignment to the reference database  (Cilleros et al. 2019). For taxa described at the genus level or higher, the number of included species is indicated within parentheses. The latest version of the dataset is available on the CEBA geoportal (http://vmcebagndev. ird. fr) under reference 5617a9ff-d0aa-48a9-b2c2-cb7fd5b92692.
Compression was performed until evidence of pressure-induced amorphisation was observed, upon which the samples were depressurised and found to return to their ambient structure. At each pressure point three 2D diffraction images were taken, which were then averaged and normalised using the FIT2D program  [47]. This 2D image was subsequently integrated using the Dioptas software  [48] , to produce a 1D diffraction pattern. The unit cell parameters were determined from the diffraction patterns by Pawley refinement in the P6 3 /mmc space group in the TOPAS Academic software  [49]. Sequential refinements were performed for the pressure measurements using the batch-analysis mode. The bulk modulus for each sample was calculated using the PASCal web application  [37]  by fitting the unit cell parameters measured within the 0-2.
1). To obtain the bulk moduli, a series of calculations were performed in which the optimised structure was compressed or expanded in 1% volume increments and then re-optimised at fixed volume before fitting the energy/volume curves to the appropriate form of the Birch-Murnaghan equation of state  [55]. As a comparison, we also evaluated the full elastic-constant matrix of FAU using the finite differences method implemented in VASP with a step size of 10 -2 Å  [56]. Lattice dynamics calculations were carried out on the optimised unit cell of EMT and the optimised primitive and equivalent conventional cells of FAU using the Phonopy package  [57]  with a finite-displacement step size of 10 -2 Å. Phonon density of states (DoS) curves and thermodynamic functions were calculated based on phonon frequencies interpolated onto a regular Γ-centered grid of q-points with 24 × 24 × 24 subdivisions.
Acknowledgments: A. S. thanks the  Royal Society  for funding. A. N. and P. R. R. thank the  EPSRC  for funding under grant no. EP/K004956/1. J. M. S. also thanks the  EPSRC  for funding ( EP/P007821/1 ) and the  University of Manchester  for the award of a Presidential Fellowship. The high-pressure experiments in this work were performed on beamlines ID27 and ID15B at the  European Synchrotron Radiation Facility (ESRF), Grenoble, France. The computational modelling was primarily carried out on the  UK Archer facility , accessed through the  UK Materials Chemistry Consortium , which is funded by the  EPSRC  ( EP/L000202 ). Calculations were also performed on the Balena HPC cluster at the  University of Bath , which is maintained by  Bath University Computing Services.
Acknowledgments We also would like to thank  Patrick Brest  and  Lionel Arnaud  for stimulating discussions and  Jean Kanellopoulos  for critical reading of the manuscript. The funding sources for this work were  Institut National du Cancer (INCa) ,  Canceropole PACA, INSERM, CNRS, Bristol-Myers Squibb Foundation for Research in Immuno-Oncology, La Ligue contre le Cancer  and the  French Government (National Research Agency, ANR  through the  "Investments for the Future" LABEX SIGNALIFE: program  reference # ANR-11-LABX-0028-01 ).
Discussion At the global extent, by analyzing HerbDivNet data across 30 sites in 19 countries and six continents, our results reveal that the shape of the biodiversity-productivity relationship depends strongly on the level of productivity of natural grasslands. Under low-productivity conditions (corresponding to stressful habitats), Biodiversity effect on productivity Biodiversity effect on productivity Fig. 5  Shift in the sign and strength of the biodiversity effects across productivity gradients in hierarchical Bayesian models. Each point represents the mean productivity (horizontal axis) and the median value of biodiversity effect on productivity (vertical axis) at each grid (N = 151 grids) or grid group (low, medium, and high productivity groups in the inset). Vertical lines represent the 95% of credible intervals (CI) of estimated biodiversity effects.
Methods Data selection. To minimize the methodological differences that exist among disparate studies, a global survey of natural grasslands uses consistent datacollection methods for testing global hypotheses in ecology and environmental science: the Herbaceous Diversity Network (HerbDivNet)  43, 44. HerbDivNet conducts coordinated surveys in 30 natural grasslands (sites) from 19 countries and six continents (Supplementary Fig. 1 ; the map is displayed using package "maps" in R version 3. 4. 1). Grassland type was separated into 5 categories: temperate, temperate wet meadow, Mediterranean, tropical and subtropical, and alpine. At each site, between 2 and 14 grids (each 8 m × 8 m in size) were sampled, and each grid was located in a patch of relatively homogeneous environment. HerbDivNet has 10,048 plots (each 1 m × 1 m), most of which have peak live biomass, total (live+litter) biomass and species richness data (https://datadryad.
org/resource/ doi:10. 5061/dryad. 038q8). The decomposition rate of plant litter varies greatly among different sites, so that using total (live+litter) biomass would cause a large error. Therefore, we used data from the 9640 plots in which both species richness and aboveground live biomass were measured in our analysis (Fig. 3 ). The aboveground live biomass at peak biomass was used as a surrogate measure of primary productivity, and the plant species richness was used as a measure of biodiversity. These plots were distributed in 151 grids, each of which contained from 60 to 64 plots. HerbDivNet spans a wide range of plant biomass (from 2 to 3374 g m -2 , with 480 plots over 1000 g m -2 , about 5% of the 9640 plots), and provides sufficient replication of plots along the productivity gradient.
6d ), where we replaced log (σ k ) with log (CV k ) in Eq. 1. We fitted all the Bayesian models using Markov Chain Monte Carlo (MCMC) sampling techniques in JAGS 4. 2. 0 using the rjags package  62. We set diffuse prior distributions for all parameters (see Supplementary Materials for JAGS code). We ran three parallel chains and used Gelman and Rubin's convergence diagnostics to assess parameter convergence (with a cutoff value of 1. 1)  63. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements This work was generated using data from the  Herbaceous Diversity Network  (https://datadryad. org/resource/doi: 10. 5061/dryad. 038q8 ). We are grateful to all of the participants of HerbDivNet who have contributed data. We thank  Peter B. Adler ,  Fangliang He ,  Wensheng Shu , and  Chengjin Chu  for helpful comments during the preparation of the manuscript. Y. W. was supported by the  National Natural Science Foundation of China  (Project  31170398 ,  30970472 , and  31230013 ). M. L. was supported by the  TULIP Laboratory of Excellence  ( ANR-10-LABX-41 ) and by the  BIOSTASES Advanced Grant , funded by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  ( 666971 ).
Data availability The grassland data are from Fraser et al. 44  ; these data have been deposited by Fraser et al. in the Dryad repository (https://datadryad. org/resource/doi:10. 5061/dryad. 038q8; Title: raw plot data from globally distributed sites). Temperature and precipitation data are from WorldClim (http://worldclim. org/version2), and daylight hours data are from 'NASA Surface Meteorology and Solar Energy: Global Data Sets' (https://eosweb. larc. nasa. gov/cgi-bin/sse/global. cgi). The source data underlying Figs. 2, 3, 4a, b, 5, 6a-d, 7a, b, and Supplementary Figs. 1, 2a-f, 3a-f, 5a, b, and 6a, b are provided as a Source Data file.
It is then compressed in the axial direction at constant radial stress. The loading is reversed until the isotropic stress state is reached and reloaded at several stages of the shearing. At chosen states, mainly when the loading direction is changed, the fabric of the specimen (positions of the spheres and contact orientations) is recorded. The macroscopic behaviour of the specimen is shown in Figure  1. As in  [6] , images are created from these states using Kalisphera  [8] , a tool to create images of spheres mathematically correct regarding the partial volume effect  [9]. Inherent image deficiencies such as noise and blur are applied to the images to obtain realistic representations of real tomographies, see  [5]  for further details. The image analysis is carried out with the open source software spam  [10].
The image analysis is carried out as described above for the synthetic images from DEM using the image analysis software spam. The evolution of contact as well as particle orientations is depicted Figure  4  as Lambert azprojection plots for the marked in Figure  3. The particle orientations are calculated from the moment of inertia and its eigenvectors. The eigenvector corresponding to the largest eigenvalue represents the smallest representative length of the particle. Due to the shape of lentils, this eigenvector is considered to be the significant particle orientation and plotted in Figure  4. The particle fabric agrees well with the fabric that was targeted in the preparation of this specimen, an inherent 45 • angle towards the vertical direction. The contact fabric is more spread, but also aligned to this targeted initial anisotropy.