dataesr/scientific-paragraphs-categorization huggingface dataset:
Gradients in the Zone Figure  8a  shows turbidity and RS (red) values along the coastline of HIQ. In the RS (red) dataset, the highest values were observed in Thetis Bay, along the N coast of the Island, and around Collinson Head (RS (red) ~0. 08). This indicates that the sediment supply along the SE coast, which is dominated by retrogressive thaw slumps, is comparable to the one along the NE coast, which is dominated by steep cliffs. Lower values were detected in Pauline Cove and west of Avadlek Spit (RS (red) 0. 04-0. 05), which are the areas, where the cf-mask did not perform adequately. Thus, these lower values might not reflect reality. The overall variation of RS values is low, except in the two aforementioned regions. In the turbidity dataset modelled according to  [43] , the turbidity values along the N coast (up to 500 FNU) are remarkably higher than in Thetis Bay (up to 200 FNU) or in the Workboat Passage (up to 100 FNU).
The turbidity values also decrease considerably from 400 FNU at Collinson Head to 50 FNU at Simpson Point. This indicates, in contrast to the RS dataset that the eroding permafrost cliffs along the NE coast provide higher sediment supply to the nearshore zone than retrogressive thaw slumps along the SE coast. The variation of turbidity values around the island is larger compared to the variation of the corresponding RS (red) value, which is due to the exponential design of the turbidity algorithm (Figure  8b ). Figure  8b  shows the correlation of the turbidity value with the corresponding RS (red) value displayed in Figure  8a. The correlation of both values is decent, especially for turbidity values below 250 FNU. As previously reported in the literature, the red band saturates at higher turbidity values, which results in a stronger noise  [43].
During steady ESE wind conditions, BT values are remarkably higher at the SE and SW coast than during NW wind conditions (~10 °C compared to ~6 °C). Along the NW coast, the BT are higher during steady NW wind conditions (~5 °C compared to ~3 °C during steady ESE wind conditions). Along the N coast, BT do not differ remarkably during the two wind conditions. In the turbidity dataset modelled according to  [43] , the turbidity values along the N coast (up to 500 FNU) are remarkably higher than in Thetis Bay (up to 200 FNU) or in the Workboat Passage (up to 100 FNU). The turbidity values also decrease considerably from 400 FNU at Collinson Head to 50 FNU at Simpson Point. This indicates, in contrast to the RS dataset that the eroding permafrost cliffs along the NE coast provide higher sediment supply to the nearshore zone than retrogressive thaw slumps along the SE coast.
025). Figure  9e ,f show the turbidity gradients during steady NW and steady ESE wind conditions, respectively. Each transect shows a decline of turbidity with increasing distance off the coastline. The only exception is the SW during steady NW wind conditions, where the turbidity does not decline remarkably. In close distance to the coastline, the slope of the declining transect lines is steeper compared to the RS (red) dataset. During steady NW wind conditions, turbidity reaches a background level in 750 m distance off the coast (~5 FNU). During steady ESE wind conditions, turbidity reaches a background level in 1500 m distance off the coast  (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20). High turbidity values (>400 FNU) were detected very close to the N, NE, NW, and SE coast during steady ESE wind conditions.
The strong gradient of turbidity from the nearshore to the offshore zone under both steady ESE and steady NW wind conditions indicates that the bulk part of eroded sediment does not remain at the water surface and gets transported alongshore or rapidly settles at the seafloor. The only stable pathway of sediment that get transported to the offshore was identified NNW of HIQ. During steady ESE wind conditions, another pathway was identified SE of the island toward Herschel Basin. In this study, we showed that Landsat satellite imagery provides coastal observations (e. g. , temperature and turbidity within surface waters) at a high spatial resolution in contrast to the coarser spatial resolution of ocean color satellite sensors. Its high spatial resolution and the long time series of spatially consistent data (since 1982) are unique to resolve hydrodynamic processes close to the shoreline and to compare data ranging over more than three decades.
Remote Sens. 2019, 11, x FOR PEER REVIEW 8 of 22 clockwise around HIQ (Figure 4). This line consists of points with a spacing of 250 m, resulting in 235 points in total. The distance of each point to the coastline is 75 m, meaning there are two pixels between the USGS-cf mask coastline and the sampling point to avoid mixing pixels of land and water. The value to point function in the spatial analyst toolbox in ArcMap 10. 4. 1 was used to extract RS (red) and turbidity values at each point of the transect. The extracted points were plotted against each other using Python.
In this study, TSMP is run for the European CORDEX domain  17  as a first step to establish a terrestrial systems climatology for the past decades, with a focus on a physically consistent representation of variably saturated groundwater and overland flow coupled with land surface and atmospheric processes. The dataset we present features daily simulation results since January 1989, but as some grid cells only reach the groundwater equilibrium in 1995, the period applicable for analysis consists of 23 water-years from September 1996 to August 2018 of all essential variables to describe the terrestrial water and energy cycles (Online-only Table  1 ). The TSMP-G2A data set is a valuable innovative data set to analyze and understand the mechanisms and interactions of water and energy in the terrestrial system including extreme events such as heat waves and droughts.
Surface runoff is calculated by a kinematic wave equation in ParFlow  26. This leads to a dynamic coupling of land surface processes and 3D variably saturated groundwater flow 3D heterogeneity in soil and hydrogeologic hydraulic properties. The component models are coupled using OASIS3-MCT  13  , following a Multiple Program Multiple Data (MPMD) paradigm in an efficient parallel approach for massively parallel supercomputer environments  18. Hence all simulations of this study are based on TSMP in fully coupled mode including ParFlow, CLM and COSMO. Model setup. The model is set up over the European continent (Fig. 1 ), using a rotated latitude-longitude model grid with a horizontal resolution of 0. 11° (12. 5 km, termed as the "EUR-11" grid) from the COordinated Regional Downscaling EXperiment (CORDEX) project  17, 27, 28  , to ensure consistency in comparison with the ensemble of CORDEX RCM experiments (Table  1 ).
The time step for ParFlow and CLM is 15 minutes, while COSMO runs with a 60 seconds time step. The coupling frequency between the component models is 15 minutes using averaged values from COSMO. The hydraulic conductivity parameters for ParFlow are estimated  31  based on the soil texture taken from the Food and Agricultural Organization (FAO) database  32. Fifteen different soil types condition the permeability, so that, e. g. , grid cells with a soil dominated by clay have a vertical permeability of 0. 062 m/hr, while for those composed mainly of sand, a vertical permeability of 0. 27 m/hr is defined. The horizontal permeability values are scaled by a factor of 1000, following the scaling effect of the hydraulic parameter according to the grid resolution, resulting from the loss of information of the terrain curvature as consequence of spatial aggregation  33.
This forcing consists of an annual time series of 6-hourly time steps at each grid point, averaged over 11 years (1979-1989). The reanalysis data were retrieved from the European Centre for Medium-Range Forecasting (ECMWF) MARS archive. The ERA-Interim variables specific humidity, air temperature, 10 m wind speed, precipitation, long and short wave as well as the geopotential height at 0. 7° lateral resolution at the lowest ERA-Interim model level were resampled to the EUR-11 grid of TSMP using the COSMO "int2lm" pre-processing software (Fig. 2 , top left boxes). A stable dynamic equilibrium with regard to, e. g. , soil moisture and groundwater states was achieved after running the ParFlow-CLM model system in a closed loop for 20 cycles. One cycle means one year simulation (January to December) driven by the above mentioned data set.
After 20 cycles, the surface and subsurface model states converge and then constitute the initial surface and subsurface conditions for the fully coupled simulation starting from 1989-01-01. The model initialisation in 1989 makes the simulations compatible with the experiment protocol of the EURO-CORDEX RCM ensemble experiments. The ERA-Interim reanalysis data is also used for the COSMO model atmospheric initial and lateral boundary conditions for the EUR-11 domain throughout the fully coupled model simulations. In order to update the lateral boundaries more frequently than the available 00 and 12 UTC analyses, additional 3, 6, 9 and 15, 18 and 21 UTC forecasts from ERA-Interim were estimated by linear interpolation via the MARS retrieval system of ECMWF to inform the model every 3 h along the boundaries.
The raw outputs from the three component models are archived with 3 h frequency in monthly netCDF files. In addition to data format conversion (binary files from ParFlow to netCDF) and the reduction of the number of files by merging the data in monthly files, post processing also consists of temporal aggregation, calculating daily, monthly and seasonal temporal averages using primarily the Climate Data Operators (CDO, available at http:// www. mpimet. mpg. de/cdo). Boundary relaxation zones are removed from each side of the domain. In order to efficiently exchange data and as a means of data provenance tracking, final outputs are transferred to be as much as possible compliant with the CORDEX Archive Design, which in turn is derived from the CMIP specifications. In the process of "CMORization", data are stored using a predefined Data Reference Syntax (DRS) for the paths and filenames, and defined meta-data per variable as well as global attributes to describe the experiment.
Data records The data set is available as netCDF V3 files without compression and is stored at a persistent data repository at the Jülich Supercomputing Centre 36 , as well as at PANGAEA  37. The spatial resolution and grid specification corresponds to the EURO-CORDEX EUR-11 domain, according to the CORDEX data protocol specification (Version 3. 1, 3 March 2014, http://is-enes-data. github. io/cordex_archive_specifications. pdf), with 424 × 412 grid elements on the rotated 0. 11° grid. We provide time-series of daily means, aggregated into yearly files. The file Fig. 2  TSMP workflow for climate simulation experiments. Tasks and data flow are presented from the extraction of initial conditions (IC), atmospheric forcing for spin-up and boundary conditions (BC) every 3 h, through pre-processing COSMO inputs with int2lm, model spin-up, climate simulations with TSMP (run TSMP) down to the visualisation and analysis, also showing in which steps input and output data are stored on the Jülich Supercomputing Centre tape archive system ($ARCHIVE).
Limitations. Due to the fact that the simulation was run transient after initialisation in 1989-01-01, without re-initializations, the model solution is expected to diverge from the forcing data as well as observations at the event scale. However, the regional anomalies compared to reference observational datasets show that the model captures the system dynamics and succession of dry/wet years, as well as heat waves and cold spells (Figs. 3  and 4 ). Furthermore, the 12. 5 km resolution is not high enough to explicitly resolve convection and the development of convective precipitation  38, 39  or the hydrology of smaller headwater catchments, including flash-flood prone watersheds.
technical Validation The current experiment was designed to produce a near-natural climatology of the physical states of the terrestrial system, without the influence of, e. g. , human water use. Accordingly, no real-world measurements are available for this type of system state for comparison and validation. Nevertheless, Figs. 3  and 4  show that the model reproduces the succession of warm/cold and wet/dry seasons on the regional scale for the PRUDENCE analysis regions  40  (boxes in Fig. 1 ), compared to the one of the commonly used reference datasets for temperature and precipitation, the 0. 25 degrees gridded European Climate Assessment and Dataset 41 (E-OBS v19, ECA&D). The Pearson's correlation values between the simulated and observed data series show good agreement in most European regions, with scores ranging from 0.
Monthly anomalies were calculated for each pixel and then within each PRUDENCE region. Figure  5  shows that the column water storage simulated by TSMP is in good agreement with the GRACE data in most PRUDENCE regions, but there are discrepancies in the Alpine region (AL) as well as in Scandinavia (SC). The model capability of reproducing water storage and water table depth have been discussed in previous studies focusing on the European heat wave of 2003  15, 16. The overall WTDs simulated with TSMP are comparable to the WTD composition by an observation based global gridded model of WTD  44  with large-scale patterns, following the terrain, representing a shallow WTD along the coastlines and in arid valleys and also inundated wetlands in lowland regions, e. g. , Netherlands  15.
Usage Notes As most climate data in netCDF, starting with a quick visualization can be easily achieved with any netCDF viewer such as ncview (http://meteora. ucsd. edu/~pierce/ncview_home_page. html), meta data of the file may be best viewed using the ncdump command; a list of software tools and libraries for using netCDF is available from the developers of the netCDF format at UCAR (https://www. unidata. ucar. edu/software/netcdf/software. html). The CDO software is a collection of operators for standard processing of climate model data and can be directly used to work with this dataset taking into account the spatial reference of the data as well as the temporal information. The application is straightforward and allows a wide range of calculations from space-time aggregation to sophisticated climate index calculations.
For more personalized analysis and visualization, we also recommend using Python with specific libraries such as Pandas or xarray. Codes created specifically for post-processing TSMP are also available with the TSMP release version. post-processing tools. TSMP is essentially released without its component models, i. e. , the release contains the built system, all configuration files, such as namelists, for the sample cases, the component model code patches and all coupler related modifications. The user must download the component models from their respective separate repositories: All ParFlow releases are available via GitHub (https://github. com/parflow/parflow). The official CLM website (http://www. cgd. ucar. edu/tss/clm/distribution/clm3. 5/index. html) offers all links to.
names are structured according to the Data Reference Syntax as defined by the EURO-CORDEX archive design: <variable>_<spatial resolution>_<boundary conditions dataset>_<period>_<run identification>_<insti-tute>_<model>_<data version>_<time step>_<initial time step>_<final time step>. For example, the file clw_EUR-11_ECMWF-ERAINT_evaluation_r1i1p1_FZJ-IBG3-TSMP11_v1_ day_20070101-20071231. nc is the vertical integrated cloud ice variable, at the EUR-11 resolution (12. 5 km). The run used ECMWF ERA-Interim data, ensemble member r1i1p1, as boundary conditions during the evaluation period, performed at the Research Centre Jülich (FZJ) at IBG-3 Institute, using TSMP version 1. 1. It corresponds to the first data-set version (v1), and the file contains daily values between 2007-01-01 and 2007-12-31.
acknowledgements The authors gratefully acknowledge the computing time granted through JARA-HPC and the VSR commission on the supercomputer JURECA at Forschungszentrum Jülich through compute time projects cjibg33 and cjibg35. The work described in this paper has received funding from the Initiative and Networking Fund of the Helmholtz Association (HGF) through the project "Advanced Earth System Modelling Capacity (ESM)". The content of the paper is the sole responsibility of the author(s) and it does not represent the opinion of the Helmholtz Association, and the Helmholtz Association is not responsible for any use that might be made of the information contained.
Spin polarization was not taken into account since it was shown to be unnecessary for such Al-rich complex intermetallic compounds  [60]. The structures were plotted using the VESTA software  [61]. Bulk structures were relaxed till the Hellmann-Feynman forces were as low as 0. 02 eV/Å. The resulting theoretical bulk parameters are found in good agreement with the experimental ones. As shown by Table  S1 , the relative differences are smaller than 1% (resp. 2%) for Al 13 Co 4 and Al 13 Ru 4 (resp. Al 13 Fe 4 ). Adsorption studies were performed using surfaces modeled with 7-layerthick symmetric slabs separated by void (thickness ca. 12 Å). The positions of all the atoms in the slab were optimized, while the volume and the shape of the simulation cell were set. The adsorption energy for atomic hydrogen [E ads (H)], and 1,3-butadiene or 1-butene [E ads (but)] was calculated as the sum of the energies of the relaxed surface and the gas-phase molecule minus the energy of the adsorbate-surface system: E ads H ð Þ ¼ E slab ð Þ þ 1 = 2 E H 2 ð Þ À E slab þ H ð Þ E ads but ð Þ ¼ E slab ð Þ þE but ð ÞÀE slab þ but ð Þ where E(slab), E(H 2 ), E(but), E(slab+H) and E(slab+but) are the energies of the systems {slab}, {dihydrogen molecule}, {unsaturated hydrocarbon molecule}, {slab + atomic hydrogen} and {slab + unsaturated hydrocarbon molecule}, respectively.
Acknowledgments EG thanks  Dr. David Loffreda  for fruitful discussions. EG acknowledges financial support through the  COMETE  project  (COnception in silico de Matériaux pour l'EnvironnemenT et l'Energie ) funded by the  'Région Lorraine '. The work in Nancy was supported by the  European Integrated Center  for the  Development of New Metallic Alloys and Compounds. High Performance Computing resources were provided by  GENCI  under the allocation 99642, as well as the EXPLOR center hosted by the  Université de Lorraine  (allocation 2017M4XXX0108).
EBSD analysis A CamScan X500FE Crystal Probe equipped with an Oxford/Nordlys EBSD detector at Géosciences Montpellier at the University of Montpellier in France was used to obtain the crystallographic orientations of the elongated aggregate of garnet fragments with growth rims in the cataclasite (Fig. 3A ) and the fragmented garnet and diopside in the damage zone (Fig. 3, B  and C ). The EBSD was run with an accelerating voltage of 18 to 20 kV, a sample tilt of 70°, and a working distance of 25 mm. A step size of 0. 4 m was used for the aggregate of garnet fragments, and a step size of 1 m was used for both the fragmented garnet and diopside grains. The MATLAB toolbox MTEX (version 5. 1. 1) was used to construct the EBSD maps and perform grain analysis. Grains and subgrains less than 3 to 5 m were removed.
High-resolution x-ray microtomography An x-ray tomography scan of the core sample was acquired at the European Synchrotron Radiation Facility, beamline ID19, with a voxel size of 6. 5 m and an energy of 92 keV. Data processing and three-dimensional (3D) rendering were performed using the software AvizoFire. The images in Figs. 1D  and 2 (A  and B ) were colored by density. The outer pseudotachylyte in Fig. 1D  was made transparent for better 3D rendering. The direction of elongation of garnets at the boundary between the cataclasite and outer pseudotachylyte allowed for the estimation of a slip direction and an angle of 31° from the topographic surface. This allowed for the calculation of the actual offset of 2 m based on an apparent offset of 1. 7 m measured in the field (fig. S1C ).
Acknowledgments: We thank  C. Nevado  and  D. Delmas  for high-quality thin-section polishing,  F. Barou  for the EBSD measurements,  B. Cordonnier  for the acquisition of the x-ray tomography data,  W. A. Griffith  for providing suggestions on an early version of the manuscript, and two anonymous reviewers for their very thorough reviews that improved our paper. Funding: This project has been supported by the  European Research Council (ERC).
2 ± 0. 1 arcsec) and a region of ≈ 20 arcsec around each quasar with the ≈ 1 × 1 arcmin 2 field of view of MUSE (see Figure  1 ). We collected a total of 102 exposures, 19 of which were obtained in August 2017 with integration times of 1200 s each, plus 83 exposures lasting 1450 s each. The total exposure time for the data presented in this work is therefore 39. 7 hours. A small dither pattern with ≈ 1 arcsec offsets and 10 degree incremental rotations has been applied to ensure optimal sampling of the field of view and to allow for better control of systematic errors associated with the uneven response across the different spectrographs. Weather conditions during the runs were on average good, with clear sky and sub-arcsecond seeing (≈ 0. 6 -0. 8 arcsec) for the majority of the exposures.
ACKNOWLEDGEMENTS We thank the anonymous reviewer for useful comments. EL is supported by a  European Union   COFUND/Durham Junior Research Fellowship  (grant agreement no. 609412 ). We acknowledge support by the  Science and Technology Facilities Council  [grant number  ST/P000541/1 ] and from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement nos. 717001  and  757535 ). This work is based on observations collected at  ESO/VLT  ( ID 1100. A-0528 ). SC gratefully acknowledges support from  Swiss National Science Foundation  grant  PP00P2 163824. RJC was supported by a  Royal Society University Research Fellowship.
5°C for 1 minute, and 40 cycles at 97°C for 30 seconds and 59. 7°C for 1 minute. RT-qPCR raw data were analyzed using SDS 2. 4 and RQ Manager Software (Thermo Fisher Scientific). The differential expression of significantly deregulated miRNAs (p-value < 0. 05) was further validated by RT-qPCR in the entire dataset (47 CRC and 47 NCT) according to  [20]. Briefly, the cDNA synthesis was performed as described above. The PCR reactions were carried out in final volumes of 10 μl using the Applied Biosystems 7900HT Fast Real-Time PCR System (Thermo Fisher Scientific). Reaction mix consisted of 54 ng of reverse-transcribed RNA, 1x TaqMan® Universal PCR Master Mix, 0. 2 mM TaqMan® primer-probe mix (Thermo Fisher Scientific). An RT-negative control was included in each batch of reactions. Cycling conditions were: 10 minutes of denaturation at 95°C, 40 cycles at 95°C for 15 seconds and at 60°C for 1 minute.
Identification of miRNA experimental gene targets The target genes of CRC-related to differentially expressed miRNAs were predicted by seven algorithms: DianaMicroT_strict  [21] , miRanda-mirSVR_S_C  [22] , MirTarget2  [23] , picTar_chicken  [24] , PITA_Top  [25] , starBase  [26]  and TargetScan_v6. 2  [27]. Experimentally validated targets were identified by literature and/or from miRecords  [28]  and mirTarBase v4. 5  [29]  databases. Comparisons of target genes lists were performed with custom scripts using the computing environment R  [30]. Targets predicted by at least three of the seven algorithms or previously experimentally validated (i. e. reported in at least one database or in literature) were selected for subsequent analysis, in order to obtain improved results. To inspect the function of the differentially expressed miRNAs, the target genes were submitted to Gene Ontology (GO) and KEGG pathway enrichment analysis using ToppCluster (https:// toppcluster.