dataesr/scientific-paragraphs-categorization huggingface dataset:
DATA AVAILABILITY ChIP-seq data (FastQ and Bigwig files) are accessible in the NCBI Gene Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo/) under the accession number GSE109619. In addition to our own ChIP-seq data, previously published datasets were retrieved from the Gene Expression Omnibus (GEO) database for re-analysis and comparisons. Sequencing datasets were thus retrieved for: human hematopoietic cells enriched within the immature cell surface marker CD34+ (HSC-CD34 + ) H3K27me3 (GSM772951), HSC-CD34 + H3K4me3 (GSM773041), (HSC-CD34 + ) H3K27ac. In addition, expression data from the HSC CD34+ cells of healthy donor (GSM308470) and expression data from human untreated KG1 cells (GSM1316691) were extracted.
ACKNOWLEDGEMENTS Authors thank  Dr Vincent Geli  and  Dr Ari Melnick  for helpful discussions. Authors gratefully acknowledge the advice of  Prof. Atsushi Iwama  during revision of the manuscript. Authors gratefully acknowledge  Montpellier MGX  and  MIT Biomicro Center Sequencing  facilities for the ChIP-seq sequencing and the  CRCM DISC  and  Cibi Platforms  for the bioinformatics support.
Therefore, the calibration of the tunnel support of the tunnel in poroelastic anisotropic medium should be considered with the hydro-mechanical model and the stress state in larger stiffness direction as well as the deconfinement rate of support installation so that the support/liner meets at once requirements of bearing and limiting the convergence and the plastic deformation zone around the tunnel. This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 107. 01-2017. 307.
Acknowledgements Y. L. was supported by a Ph. D. fellowship from the  Chinese Scholarship Council. A. K. H. H. received funding from the  Dutch Ministry of Education, Culture and Science (Gravitation Program   024. 001. 035 ) and gratefully acknowledges the  Netherlands Organisation for Scientific Research  ( VIDI grant ). The authors gratefully acknowledge fruitful discussions with  Prof. J. -M. Lehn.
Plants from 10 ecotypes were grown under controlled conditions. First, a juvenile ramet without roots (mother ramet) was transplanted into a pot containing field soil. Plant growth was oriented by forcing the newly emitted ramets (daughter ramets) of the two ramifications to root into separate pots containing sterilized substrate (Fig. 1 ). Our aim was to detect the endophytic microorganisms present in the mother ramet roots and transferred to the daughter ramets through the clone stolons. High-throughput amplicon sequencing of 16S and 18S rRNA genes was used to detect and identify bacteria, archaea, and fungi within the root endosphere and the stolon internodes. Control pots randomly distributed in the experiment were also analyzed to remove from the dataset all operational taxonomic units (OTUs) which could not be attributed to a plant-mediated transfer of microorganisms (see methods in Additional file 1).
Sequencing and data trimming All PCR amplification products were purified using Agencourt AMPure XP kit. After purification, the amplification products were quantified and their qualities checked using Agilent high sensitivity DNA chip for BioAnalyzer (Agilent) and Invitrogen fluorimetric quantification (Quant-iT PicoGreen dsDNA Assay Kit, ThermoFisher Scientific). Quality was estimated by the size of the amplicons produced regarding the expected size, the absence of primers dimers, and the molarity and concentration of the PCR products. All PCR amplifications products were then subjected to an end repair step and adaptor ligation using the NEBNext Ultra II DNA Library Prep Kit for Illumina (New England Biolabs). Multiplexing was done with a PCR step using NEB next Ultra 2 multiplex oligo (dual index, New England Biolabs).
Multiplexed products were then quantified and quality checked using Agilent high sensitivity DNA chip for bioanalyzer and quantitative PCR with SmartChip martchip RT PCR (Takara-Wafergen). Amplicons libraries were pooled to equimolar concentration, a quantitative PCR using LightCycler 480 SYBR Green I Master (Roche) was performed and products were paired-end sequenced (2 × 250 bp) with an Illumina MiSeq instrument at the Human and Environmental Genomic Plateforme of Rennes (France). Data trimming consisted of different steps: primer removal (Cutadapt) and suppression of sequences containing unidentified bases. An additional step consisted of checking the sequence orientation using a homemade script. This stringent data trimming resulted in 9,592,312 reads. Trimmed sequences were then analyzed using the FROGS pipeline  [30]  (bio-informatic workbench "X.
The sequencing depth was high enough to describe the microbial communities in detail (Additional file 1: Figure  S1 ). To homogenize the number of reads by sample for subsequent statistical analyses, samples were normalized to the same number of reads based on graphical observation of the rarefaction curves using the same R package. During this step, samples with less reads than the normalization value were removed from the dataset. All OTUs found in the soil of the control pots were then removed from the data set. The three control pots contained 0 archaeal reads. Two out of the three control pots contained 0 fungal reads and we found a total of 3371 fungal reads distributed in 19 OTUs in the last pot. The three control pots also contained 65,378, 33,773, and 37,587 bacterial reads distributed respectively in 153, 313, and 219 OTUs.
Statistical analyses The positions and stolon of each ramet within the network were recorded as two factors for the statistical analyses. We considered three positions in the network: the mother ramet, the 1st daughter ramet and the 2nd daughter ramet. The stolon was considered as a factor with two levels: the 1st and the 2nd stolon emitted during growth. We analyzed heritability, richness and composition of microorganism assemblages in G. hederacea ecotypes. We analyzed fungi and bacteria assemblages separately. No statistical analyses were performed on archaea data as they were found in the mother ramet roots and in the stolon internodes following the mother ramets but not in the daughter roots. All statistical analyses were performed using the R software  [35].
e. , random dispersal of OTUs), we used a null model approach in which the identity of the fungi or bacteria species in the experimental samples was randomized while keeping the OTU richness identical. For each ecotype, we thus generated bacterial and fungal random daughter communities by sampling species from all the mother roots communities (regional pool) and compared the observed heritability in our dataset to this distribution of random heritability values. The null model approach indicated that the observed communities displayed significantly higher OTUs heritability between the roots of mothers and daughters than expected stochastically (one sample t test with alternative hypothesis "higher," P < 0. 01 t = 3. 03, df = 8, and P < 0. 001 t = 6. 11, df = 9 for fungi and bacteria respectively) (Additional file 1: Figure  S2 ).
The heritability of a specific cohort of microorganisms The differences in microorganism community composition between mother and daughter roots were assessed using a multi-regression approach with a partial least squares discriminant analysis procedure (PLS-DA) (see Material and Methods, Additional file 1). The advantage of this analysis is its ability to test a hypothesis based on a grouping factor of the samples in the dataset (i. e. , an explicative factor) and to obtain the significance of the factor as well as the part of the variance explained by the factor. With this analysis, the entire dataset can be used and most of the variance conserved in contrast to NMDS approaches in which the distances between samples such as Bray-Curtis or Jaccard summary the variance between samples. Significant differences in the composition of daughters communities compared to mothers' were detected for both fungi (P PLS-DA = 0.
Cadence Table  2  lists four epochs of observations of the dark vortex. In Figure  1 , we show images and maps of Neptune at each epoch. The original discovery of SDS-2015 in OPAL data led to a supplementary Midcycle program, focusing on the blue wavelengths where the vortex itself is most evident. Only 16 days before the OPAL 2015 observations, WFC3 imaging of Neptune taken in support of VLA observations (GO-14044) barely missed the dark vortex. Companion clouds were seen, but the exposures were acquired some 20-30 minutes before SDS-2015 rotated onto the disk.
Photometric Calibration For the FQ727N frames, we applied corrections to remove photometric errors from fringing, an effect expected to be in the 2%-5% range based on analysis of flat field data  (Wong 2010). The actual magnitude of the correction across the regions of the detector where Neptune was observed was 0. 5%-1. 5%, a smaller effect than predicted. This is because part of the overall 2%-5% fringing amplitude in this filter bandpass is corrected by the pipeline flat fields (obtained using a continuum light source), leaving just the error due to the difference in spectral energy distribution between the calibration source and Neptune's scattered solar spectrum. We also minimized the fringing amplitude by placing Neptune on an area of the detector where fringing is relatively smooth over the 50 pixel size scale of Neptune's disk.
This method could not be used for the 2015. 71 data, because SDS-2015 was positioned so close to the limb in some data frames that part of the vortex became indistinguishable from the surrounding atmosphere. To compensate for this effect, we manually determined the vortex position in each frame in the 2015. 71 epoch, using WinJUPOS software. 17 For all measurements using the HST data, we estimate an uncertainty of about 10ms -1 , including error contributions from image navigation uncertainty, noise in the image, and feature centroiding. Note. Centers and widths are determined by measuring half-power points in brightness profiles (Figure  2  top) in the F467M data. Multiple images are combined per epoch to achieve higher signal to noise; this requires combining data after correction for limb darkening (using Equation (1) and values in Table  3 ).
Other observatories have contributed time-domain observations of the atmospheres of Neptune and other giant planets. Kepler K2 observations produced long-duration, short-cadence photometry  (Simon et al. 2016). The resulting light curve data are valuable for comparison with brown dwarf and exoplanet variability, because the K2 Neptune observations were tied to resolved imaging data to interpret the simple photometric variation. A similar effort was done using Spitzer photometry  (Stauffer et al. 2016). Twilight adaptive-optics imaging programs are building up a new campaign of frequent snapshot observations at Lick and Keck observatories  (Molter et al. 2017) , providing a higher-resolution complement to ground-based amateur coverage  (Hueso et al. 2017). Hypothetically, if OPAL 2017 observations had been too late to detect a small companion cloud centered over a very lowcontrast dark spot, there would be a major open question: is the massive equatorial cloud activity in 2017  (Molter et al.
Two additional datasets of rainfall isotopic signatures, retrieved from the Global Network of Isotopes in Precipitation (GNIP; IAEA/WMO; 2016; accessible at: http://www. iaea. org/ water) were used in the analysis of our results. The first dataset describes long-term seasonal and monthly accumulated rainfall volumes (referred to as the 'Cayenne data') measured in Cayenne (~90 km away from our sampling site). The second dataset contains the seasonal pattern of the corresponding isotopic signature (δ 18 O and δ 2 H) as observed in Suzini (district located in Cayenne) for the monthly accumulated rainfall volumes in the year 1995 (referred to as 'Cayenne 1995 data'; GNIP 2016). Due to their proximity to Paracou, the comparable meteorological conditions, height above sea level (a. s. l. ) and similarity in position relative to the coast in comparison to the Paracou field station, both datasets were assumed appropriate for use in our study.
Sampling setup Within each edaphic habitat (~1 ha) soil cores were sampled at eight depths (0. 1, 0. 2, 0. 3, 0. 45, 0. 6, 0. 9, 1. 2 and 1. 8 m) in close proximitry (<2 m) to the selected trees using a soil auger. A total of six soil cores per edaphic habitat were collected. It is important to note that groundwater was reached in the sandy soils below 1 m, which influences the isotopic profile of soil water. Since small catchment areas are considered in our dry season study, we assume that the isotopic composition of the stream is a proxy for ground water isotopic signature  (Kendall and Coplen 2001). Although one precipitation event occurred (16. 8 mm on 8 October; see Figure  S1  available as Supplementary Data at Tree Physiology Online) between two sample collection days, no significant difference in the measured isotopic signatures of canopy lianas, trees and soil were observed before and after the rainfall event.
The measured monthly mean δ 2 H values from historical precipitation samples (Cayenne 1995 dataset) during the sampling period (September-October) were recorded within the interquartile range (IQR, Figure  2  cyan shaded box) of the precipitation δ 2 H LMWLi values sampled in Paracou. This finding justifies using the Cayenne 1995 dataset in this analysis. Additionally, it was observed that the δ 2 H LMWLi liana xylem signatures in both plots (Figure  2 ) correspond aproximately to the δ 2 H values from historical precipitation samples (Cayenne 1995 dataset) measured in the dry season (September-October) in Cayenne. Identical correspondences were also found for the δ 18 O LMWLi signatures (data not shown). However, xylem water isotopic signatures derived from the sampled trees were significantly (P < 0.
Seasonality in water use of trees and lianas The δ 2 H LMWLi of liana xylem water, having a close correspondence with the historical precipitation isotopic signatures of the Cayenne 1995 dataset (Figure  2 ), indicates that sampled canopy lianas are seemingly able to access dry season precipitation. The occurrence of the historical precipitation signature being close to, but not encompassed by, the IQR of the δ 2 H LMWLi of the lianas indicates a marginal contribution of depleted soil water found in deeper soil layers. In consideration of the timedependent infiltration capacity of dry season rainfall, resulting in infiltration only into shallow soil layers and litter decomposition layers (see Figure  S1  available as Supplementary Data at Tree Physiology Online) before complete evaporation loss, these findings suggest that lianas take up water mainly from superficial soil layers.
In strong contrast to our observations for canopy lianas, xylem samples from trees show depleted δ 2 H LMWLi ranges when compared with the Cayenne 1995 dataset precipitation signature in the sampling period of September-October (Figure  2 ). For trees, the δ 2 H LMWLi resembles the depleted precipitation isotopic signature collected during the wet season, suggesting that sampled trees mainly depend on wet season replenishment of their tapped soil water compartments. These depleted water resources typically originate from wet season rainfall that has infiltrated to deeper soil layers  (Brooks et al. 2010 , Gaines et al. 2016 , Hervé-Fernández et al. 2016 , De Wispelaere et al. 2016).
Acknowledgments For all their highly appreciated efforts in the process of data collection, the authors like to thank  E. Kearsley ,  N. Pausenberger ,  J. Roels ,  B. Leudet  and  J. Y. Goret. We would like to thank  K. van Nieuland ,  S. Vandevoorde ,  M. Bauters ,  L. De Wispelaere ,  S. Bodé  and  H. R. Upadhayay  for laboratory and analytical assistance. The authors also like to express their gratitude to various researchers who have provided feedback on analytical and interpretative aspects of the study. In this respect we thank  J. Wright ,  S. Schnitzer ,  G. van der Heijden ,  J. McDonnell ,  F. Bongers ,  M. Combe  and  S. Moorthy Krishna Moorthy Parvathi. Additionally , we are thankful to  J. F. Corr  for English editing. Also, P. H. -F. would like to thank  M. Aigner  from the  International Atomic Energy Agency  ( IAEA ) for training in the cryogenic distillation method.
Funding This research was funded by the  European Research Council   Starting Grant 637643  (TREECLIMBERS) and benefitted from an " Investissement d'Avenir " grant of the  Agence Nationale de la Recherche  ( Centre d'étude de la biodiversité amazonienne -CEBA :  ANR-10-LABX-25-01 ). The study was conducted at the Guyaflux site, which belongs to the  SOERE F-ORE-T  and which is supported annually by  Ecofor, Allenvi  and the  French National Research Infrastructure ANAEE-F. Pedro Hervé-Fernández  is funded by  Programa de Formación de Personal Avanzado CONICYT, BECAS CHILE , by the  Commissie Wetenschappelijk Onderzoek (CWO)  and by the  Bijzonder Onderzoeksfonds (BOF) of Ghent University.
Acknowledgement: An earlier version of this article has been presented at the "Théorie critique et humanités numériques" conference, held at the University of Liège (Belgium), in November 2016. The author would like to express his gratitude to the participants and organisers of this conference as well as to his colleagues Fabien Granjon and Benoît Lelong at the Université Paris 8, France. "Theory is already practice. And practice presupposes theory. Today, everything is supposed to be practice and at the same time, there is no concept of practice. " Theodor W. Adorno and Max Horkheimer, Towards a New Manifesto. The last fifteen years have seen the rise of a new category within the humanities: that of digital humanities (DH)  (Schreibman, Siemens and Unsworth 2004). Stemming from previous categories such as humanities computingwhich described a new movement of reflexivity about the increasingly prevalent use of computer-based techniques and tools within the humanitiesthis new formula intended to question the new ways of practicing and producing knowledge in the contemporary social and human sciences and accompanied the creation of new departments, laboratories, conferences, books, and so on  (Granjon and Magis 2015).
Section 5 is a discussion, followed by conclusions in Section 6. We used the open-source toolboxes Python  (Rossum 1995) , Ob-sPy  (Beyreuther et al. 2010; Megies et al. 2011; Krischer et al. 2015)  and obspyDMT  (Hosseini & Sigloch 2017)  for data downloading, processing and plotting all figures except Figs 1 and 11, which were generated with the open-source mapping toolbox GMT  (Wessel et al. 2013 ).
Solid black lines denote usable correlations; dotted lines denote unusable correlations and dashed lines usable but noisy correlations. Grey lines in (a) indicate plate boundaries. Figure generated with the GMT toolbox  (Wessel et al. 2013). yr  (mid-2012-mid-2015). Additional RHUM-RUM island stations on Mauritius, Rodrigues, the Seychelles, Madagascar and the Îles Éparses were not part of our investigation. In total, the RHUM-RUM array covered 2000 × 2000 km 2 of the Indian Ocean and ocean islands. This study arose from the initial discovery of timing errors of up to several minutes in 4 out of 10 stations on La Réunion (yellow triangles in Fig. 1b ). All four stations featured the same type of data logger (RefTek RT 130), in which a software failure was causing loss of GPS synchronization.
Implementation of clock corrections on the archived RHUM-RUM data and meta-data The ultimate purpose of estimating clock errors is to correct seismograms archived at the data centre such that end users can rely on downloading correctly timed data and meta-data. For implementing this timing correction, we use the polynomials fitted to the estimated clock error curves, rather than individual daily values of the clock error curves themselves. The RHUM-RUM time-series are archived as miniSEED at the RESIF data centre (http://dx. doi. org/10. 15778/RESIF. YV2011) in data records of 10-15 s length. Hence the correction procedure (Wayne Crawford, personal communication, 2017 December 17) looks up the value of a fitting curve in absolute time increments of 10-15 s and modifies the header of each data record accordingly, more specifically header fields 8 (start time of record); 16 (time correction); and 12 (bit 1: time correction applied).
The time-corrected RHUM-RUM seismograms will be made available through the RESIF data centre in 2018. Time-corrected seismograms at the data centre have a data quality flag of 'Q' whereas the uncorrected seismograms have a data quality flag of 'D'. The user experience of this clock correction might differ depending on a user's data handling software, and by the length of the time-series requested. For example, ObsPy  (Beyreuther et al. 2010; Megies et al. 2011; Krischer et al. 2015)  works with a single sampling rate per time-series. Hence if user requests 2 d of data, divided into two consecutive chunks, then the last sample of the first chunk will be wrong by the equivalent of one day's drift, whereas the first sample of the second time-series will be essentially correct, and spacing of these two samples will differ from the (constant) sampling interval within each chunk.
8a ). By comparing raw data with skew-corrected data, we find that the negative clock drift of RR12 was caused by a sign error: seismograms had mistakenly been corrected and archived using a skew value of +0. 11 s instead of -0. 11 s (the latter reported correctly by  Stähler et al. 2016). This mistake produced an induced skew of -0. 22 s, which corresponds to a clock drift of -0. 57 ms d -1. This induced clock drift is in good agreement with our measured drift of -0. 65 ms d -1. The presumable origin of the clock jumps in RR07 and RR11 is discussed in Section 5. 4. Due to the usage of 10 d CCF stacks, the date of the jumps can only be estimated within a 2 d time window. Clock error corrections for RR07, RR11 and RR12 were conducted as for OBSs without skew, see discussion of Stage 2 below.
Apparent clock jumps-true clock failures or missing data samples? In addition to linear clock drifts, our CCFs detect apparent clock jumps at three OBSs (RR07 and RR11 with skew; RR35 without skew, Fig. 8 ). If these jumps had been produced by actual jumps of the physical station clock, we would expect the clock error to return to zero upon re-synchronization with GPS after OBS recovery (GPS synchronization was successful for RR07 and RR11). The reason is that the timing of the last sample would have been corrected properly according to the skew, regardless of any clock jumps that happened earlier, as explained by Fig. 13(a). However, such a return to zero drift is not indicated by the CCFs. Hence there must be another cause for the apparent clock jumps. We ruled out the possibility of artificially induced jumps due to an incorrect skew correction.
The estimated clock drift curves of RR07 and RR11 in Fig. 8 (a) resemble the red curve in case (b) rather than in case (a). went unnoted by the logger, a wrong timing (too early) would have been assigned to all samples after the gap. Indeed, all apparent jumps have this sign towards early times (Table  1 ). Although this problem was rare (three occurrences across the entire OBS network and deployment period), the fact that it occurred in the exact same manner at three different stations (once per station) also points to a technical weakness of the data logger model used in the German DEPAS OBSs. If the jumps are caused by data gaps, we would expect no clock drift before and after the jump for OBSs subjected to a linear skew correction (RR07 and RR11), as visualized in Fig. 13(b ). Indeed we observe almost no clock drift after the jump in RR11.