dataesr/scientific-paragraphs-categorization huggingface dataset:
The following RNA extraction was conducted using a Qiagen Plant Mini Kit (Qiagen, Germany) according to the manufacturer's protocol. The RNA quality was evaluated by a NanoDrop ND-100 UV-Vis Spectrophotometer and Agilent 2100 Bioanalyzer (Agilent Technologies, Germany). cDNA libraries were constructed using an Illumina TruSeq RNA Library Prep Kit (Illumina, United States). The libraries were sequenced by an Illumina 2500 and 75 bp paired reads were clipped using the default values of Illumina software. The quality of raw data was checked by FastQC v. 0. 11. 5. 1 Low-quality data was trimmed by Trimmomatic v. 0. 36  (Bolger et al. , 2014)  with the following parameters: leading 3, trailing 3, sliding window 4: 15, minlen 30. Remaining data were pseudo-aligned against a de novo transcriptomic reference established from cDNA libraries of S.
2 Functional annotation included searches against the databases of UniProtKB/Swiss-Prot and UniRef90 with the BLASTX and BLASTP algorithms with the default e-value setting to 1e -6 , annotation of the biochemical pathway in Kyoto Encyclopedia of Genes and Genome (KEGG), prediction of protein functions based on known orthologous gene products in non-supervised Orthologous Groups (eggNOG), identification of protein domains in Pfam database with HMMER, prediction of signal peptides with SignalP, prediction of transmembrane helices with TMHMM, and prediction of gene ontology (GO) terms (based on the BLAST and Pfam annotation). All DEGs were further annotated using diamond local aligner  (Buchfink et al. , 2015)  against the National Center for Biotechnology Information (NCBI) non-redundant protein sequence (Nr) database (cutoff 1e -9 ).
Gene Expression and GO Enrichment The number of reads per complementary DNA library ranged from 19. 9 to 38. 1 million with an average of 31. 3 million reads. The averaged remapping rate to the de novo library was 87. 57%. Four comparisons yielded 3, 284 DEGs, accounting for 2. 42% of 135,959 total de novo assembled genes (Supplementary Table  S2 ). The number of contigs was more affected by light conditions than temperature. Namely, there were 1,184 DEGs between 0 • C_Light and 0 • C_Dark, and 1,735 DEGs between 4 • C_Light and 4 • C_Dark. In contrast, the two temperatures featured 148 DEGs and 217 DEGs respectively in light treatments and dark treatments. The averaged annotation rate was 8. 2% for DEGs from the UniprotKB/Swiss-Prot database (Supplementary Table  S2 ). To investigate the transcriptomic response for S.
Manual Inspection of DEGs To further study the transcriptomic responses of S. latissima in darkness, we manually analyzed and classified DEGs with annotations (UniProtKB/Swiss-Prot database as the main source, NCBI Nr database as a supplement; Figure  3 ). A full list of DEGs with annotations can be found in Supplementary Table  S4. In summary, the number of downregulated genes in darkness at both temperatures was higher than the number of upregulated genes, and increased temperature (4 • C) exacerbated the repression of gene expression in darkness compared to 0 • C. In contrast, 4 • C_Dark and 0 • C_Dark showed similar numbers of upregulated genes. The summary highlights the distinguished upand downregulated metabolisms: first, darkness induced some genes involved in the glyoxylate cycle, stress-and signalingrelated activities.
Noise and subsampling Parameter identification was also performed in the presence of noise and subsampling. For this purpose, we added noise to the pseudodata by adding a uniformly distributed noise to each data point. We used different levels of noise but always paired the level of noise of amplitude with that of phase. We tested respectively noise levels of 0. 0001 m and 0. 0001 rad, 0. 001 m and 0. 001 rad, 0. 01 m and 0. 01 rad and 0. 1 m and 0. 1 rad. In the Results section, those levels of noise are labeled with their numerical value only and not the unit; we will use ''Noise of 0. 1'' and not ''Noise of 0. 1 m and 0. 1 rad''. Those levels of noise were chosen to show the increase of impact of noise in the data. In addition, the middle values of 0. 01 m and 0. 01 rad s -1 are of the order of uncertainties that we have when we treat our data of the tidal elevation of the lagoon with the Python implementation of  Codiga (2011).
The values of 0. 1 m and 0. 1 rad s -1 correspond to extremely high noise value, since we expect tidal amplitudes of the order of 0. 4 m, and they serve to test the robustness of the method. The subsampling was performed by only keeping some pseudo-data. We removed them in the same way in the two directions and with no special consideration for the land. We respectively kept one pseudodata on 2, 4, 16 and 32 in each direction for each case, which ended up with only one pseudo-data on 4, 64, 256 and 1024, 4. We chose levels which respectively correspond to 16 275, 4143, 240, 55 and 12 available data points. This number of data will be used as a label in the experiments. Finally, we also performed a few tests with localized pseudo-data. This was done in order to see how unequally distributed data would influence the parameter determination method and how robust it was.
Data processing The whole data reduction was performed using the CLIC and MAPPING packages of the GILDAS 1  software suite  (Gildas Team, 2013). For each track, we followed the same sequence of operations consisting of a standard calibration (1), flux calibration (2), and the spectral line extraction (3). The three steps are explained in more detail now, the adopted calibration parameters and references are given in Table  2. In the first step of the standard calibration (1) the system response is calibrated by measuring a continuum on a strong calibrator. In the second step, atmospheric and instrumental phase and amplitude variations are calibrated for, using measurements of a gain calibrator (a point source with phase and amplitude that should remain constant over the observed time). The best phase rms (root-mean-square) after calibration was obtained for 2017-Apr-29 data set while 2016-Dec-11 was observed under poor conditions (Table  2 ).
The derived amplitude root-mean-squares (rms) were between ∼10 and ∼25%. In the final step of the standard calibration, the absolute amplitude scale of the data was determined based on observations of one of the secondary flux calibrators for NOEMA where fluxes are monitored against planets all along the year. Given the high frequency and the relatively poor observing conditions this flux calibration provides a global uncertainty on the order of 15%. The flux calibration (2) is carried out using Io's continuum emission. Io is commonly used as primary flux calibrator and good model predictions exist for its thermal emission. Since the goal of our project was to study line emission, we could use Io's thermal emission to improve the flux calibration as described for the last step of the standard calibration.
Atmosphere modelling The observed SO 2 and NaCl lines are fitted using the radiative transfer model used in  Moullet et al. (2010). The atmosphere is assumed to be concentrated to a band around the equator with a uniform column density within 35 • N/S latitude, similar to the distribution in  Strobel and Wolven (2001). Io's atmosphere is furthermore assumed to be in hydrostatic equilibrium, with a gas temperature horizontally and vertically uniform. Line opacity is calculated for each 0. 25 km thick layer up to two scale heights, using the transition parameters (intensity and lower level energies) from the JPL and CDMS databases  Pickett et al. (1998) ; SO 2 column density, by considering both the relative and absolute contrasts, with the relative contrasts still being primary diagnostic for the temperature.
Acknowledgements L. R. appreciates the support from the  Swedish National Space Agency (SNSA)  through grant  154/17  and the  Swedish Research Council (VR)  through grant  2017-04897. A. S. -M. , P. S. and S. T. (Cologne) have been supported via Collaborative Research Centre 956, funded by the  Deutsche Forschungsgemeinschaft (DFG ; project  ID 184018867 ) and  DFG SCHL 341/15-1  ( Cologne Center for Terahertz Spectroscopy ).
The representative results for a A. viridis cell culture shown in Figure  2  demonstrated that there was no influence of the cryopreservation duration on the cell survival. Indeed, from 8 to 87 days at -80 °C, the percentage of cells that survived cryopreservation and thawing, did not vary significantly (linear regression model; coefficient not statistically significantly different from 0, p > 0. 05). The statistical analyses done on all available data (at least 3 biological replicates) confirmed that there were no significant differences between the cryopreservation durations (p > 0. 05; data not shown). For each tested cryopreservation duration, the number of viable cells were compared to the 2 million initially cryopreserved cells. These representative results were obtained from one primary cell culture cryopreserved at 24 days after initial seeding in 7 cryotubes, each cryotube being analyzed at the end of a given cryopreservation duration (from 8 to 87 days).
Therefore, additional experiments are required to assess if longer cryopreservation durations are possible and/or to determine the maximal duration using the protocol developed in this study. If a duration limitation were to be found, a long-term storage in liquid nitrogen, or -150 • C freezers following the initial -80 • C freezing should be envisioned. In addition, we also assessed the influence of the age (time after initial seeding) of the primary cell culture on the survival at thawing. Representative results of one primary cell culture, cryopreserved at five different ages (from day 17 to day 45, Figure  3 ) revealed no significant differences (linear regression model; coefficient not statistically significantly different from 0, p > 0. 05). The statistical analyses done on all available data (3 biological replicates) confirmed that there was no influence of the culture age on the cell survival (p > 0.
05; data not shown). One primary cell culture can therefore be cryopreserved at each reseeding, as long as the cellular parameters previously defined for "healthy" A. viridis primary cell culture are maintained, i. e. , cell aggregates formation, high viability, and constant growth rate  [15]. Being able to do so, considerable amounts of cell stocks for each cell culture could be created. initially cryopreserved cells. These representative results were obtained from one primary cell culture cryopreserved at 24 days after initial seeding in 7 cryotubes, each cryotube being analyzed at the end of a given cryopreservation duration (from 8 to 87 days). No significant differences between times were found (linear regression model; r 2 = 0. 037; p = 0. 443). Cells could therefore be cryopreserved for almost 3 months without any impact on the postthaw survival compared to short-term cryopreservation, further validating the efficiency of the designed protocol.
In addition, we also assessed the influence of the age (time after initial seeding) of the primary cell culture on the survival at thawing. Representative results of one primary cell culture, cryopreserved at five different ages (from day 17 to day 45, Figure  3 ) revealed no significant differences (linear regression model; coefficient not statistically significantly different from 0, p > 0. 05). The statistical analyses done on all available data (3 biological replicates) confirmed that there was no influence of the culture age on the cell survival (p > 0. 05; data not shown). One primary cell culture can therefore be cryopreserved at each reseeding, as long as the cellular parameters previously defined for "healthy" A. viridis primary cell culture are maintained, i. e. , cell aggregates formation, high viability, and constant growth rate  [15].
Absence of Cryopreservation Impact on Cell Recovery and Functional Parameters To assess long-term cell recovery, we measured weekly, at each reseeding, the cell viability of all primary cell cultures cryopreserved. Viability of cryopreserved cells was monitored for a period going from 6 to 12 weeks after thawing and was compared to the viability of the corresponding noncryopreserved cell culture. Results of cell viability monitoring for 6 weeks after thawing for one primary cell culture cryopreserved at day 24 for 8 or 36 days are presented in Figure  4. The data show that cryopreserved cells were stably viable after thawing through time (>90% viability) with a cell viability equivalent to that of the corresponding non-cryopreserved cell culture (repeated measures ANOVA; p > 0. 05). Repeated measures ANOVA conducted on all biological replicates, confirmed no.
Absence of Cryopreservation Impact on Cell Recovery and Functional Parameters To assess long-term cell recovery, we measured weekly, at each reseeding, the cell viability of all primary cell cultures cryopreserved. Viability of cryopreserved cells was monitored for a period going from 6 to 12 weeks after thawing and was compared to the viability of the corresponding non-cryopreserved cell culture. Results of cell viability monitoring for 6 weeks after thawing for one primary cell culture cryopreserved at day 24 for 8 or 36 days are presented in Figure  4. The data show that cryopreserved cells were stably viable after thawing through time (>90% viability) with a cell viability equivalent to that of the corresponding non-cryopreserved cell culture (repeated measures ANOVA; p > 0. 05). Repeated measures ANOVA conducted on all biological replicates, confirmed no differences in the cell viability over time between non-cryopreserved and cryopreserved cells nor with the cryopreservation storage period (p > 0.
The mean of viability, over time, was maintained at 95 ± 1. 9% and at 96 ± 0. 99%, respectively in the different non-cryopreserved and cryopreserved cell cultures monitored (ANOVA; p > 0. 05; see Figure  S3a ). In addition, data obtained with the two complementary cell viability assays performed on non-cryopreserved and cryopreserved cells at different time points of the kinetics confirmed all these results, i. e. , no differences in cell viability between non-cryopreserved and cryopreserved cells and a maintenance over time of the cell viability (ANOVA; p > 0. 05; see Figure  S4 ). As a first functional parameter, the long-term cell growth was assessed. Indeed, we considered the resumption of cell cycle after cryopreservation as an essential functional parameter to explore. Non-cryopreserved and cryopreserved cells displayed a similar daily growth rate over time and independently of the cryopreservation duration (8 or 36 days) as shown by a representative cell culture in Figure  5  (repeated measures ANOVA; p > 0.
For a comparative study on CM at mineral interfaces and CM in the bulk samples, the laser of the Raman spectrometer was focused 1) below the polished surface, through the transparent minerals on the CM-mineral contacts ('Contact-CM', Spec. 1 in Fig. 3 ), and 2) on the massive matrix CM at the polished surface ('Matrix-CM', Spec. 2 in Fig. 3 ). For possible artifacts in this analytical setup, see Section 4. 4. The obtained spectral data were treated with the software "Lab Spec version 5. 58. 25". Spectra were baseline-subtracted by automatic polynomial fitting in order to remove the background fluorescence. Subsequent peak fitting and characterization was performed by peak-de-convolution using a Gaussian-Lorentzian function with 100 iterations per fit.
Acknowledgment This study was based on samples from the  ICDP FAR-DEEP  drilling project in Karelia, Russia. We thank members of the  FAR-DEEP central  science team and principal investigator  V. Melezhik  for coordinating this project and  A. Crne  for discussions about the geology of the Zaonega Formation. Y. Q is financially supported by the  National Key R &D Program  ( 2018YFC0309800 ), the  National Natural Science Foundation of China  (Grant No. 41972204 ), the  Hundred Talent Program C (Y810011BRC) of China. We highly appreciate the technical help in the  Geological Survey of Norway in Trondheim, the Centre for Geobiology in the University of Bergen  and the  Institute of Deep-Sea Science  and Engineering, CAS. M. Z acknowledges support from the  European Research Council  under the European Union's  Horizon 2020 Research and Innovation Program  (grant agreement  646894 ).
Since the first review in 2000  [8] , the ocean components of Earth System models (ESMs) have evolved considerably following the different phases of coupled model intercomparison projects, CMIP3 to CMIP5, and most recently CMIP6  [9]. Conservation of heat and salt, exact computation of sea level, and the improvement of water mass properties have been the main objectives that have led modelling groups to abandon rigid-lid formulations and time-invariant vertical grids, and to develop higher-order advection schemes. Community modelling platforms such as NEMO  [10] , MOM (https://momocean. github. io/), or POP  [11]  have been extended to offer a larger choice of grids, numerical schemes, and parameterisations. Two new ocean models with flexible grids have been introduced in CMIP6: FESOM  [12]  and MPAS  [13] , and although CMIP6 models generally use quasi-geopotential grids ('z-coordinates'), one model has terrain-following coordinates (INM-CM5)  [14]  and two models use Lagrangian hybrid coordinates following isopycnals in the interior: MOM6  [15]  and Nor-ESM  [16].
Model Data Model simulation output used in Fig. 1  can be obtained via the Earth System Grid Federation (ESGF) nodes for CMIP6 HighResMIP: HadGEM3-GC3. 1  [180] [181] [182] , ECMWF-IFS  [ 1 82 , 18 4 ] , CNRM -CM 6-1 [ 18 5, 1 86 ] , C M C C -CM2-(V)HR4  [187, 188] , EC-Earth3P  [189, 190] , MPI-ESM1-2  [191, 192] , CESM1-3  [193, 194]  and DOE-E3SM  [195]. For the CMIP6 OMIP2, an archive of the model outputs and the scripts used to process the data is available at https:// doi. org/10. 5281/zenodo. 3685918.
Funding HTH, EB, PM, PH, MJR, and MB acknowledge the  Met Office Hadley Centre Climate Programme. MJR, DP, and TA acknowledge PRIMAVERA funding received from the  European Commission  under Grant Agreement 641727 of the  Horizon 2020 research programme. EP, TK , and AY were supported by the  National Environmental Research Council (NERC) National Capability Science Multi-Centre  funding for the UK Earth System Modelling project through grant  NE/N018036/1  and the  EU Horizon 2020   CRESCENDO  project, grant number  641816. BFK is supported by  ONR   N00014-17-1-2963 ,  NSF 1350795  and  1655221 , and  NOAA NA19OAR4310366. RB was supported by  NSF 's  Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project  under  NSF  Award  PLR-1425989. DPM is supported by  NERC   NE/R000999/1.
1 ). We used the wellestablished open-source software, large-scale atomic or molecular massively parallel simulator (LAMMPS), to obtain the initial velocities used as input for the inertial model after laser excitation. The calculations were performed using a cell size of 8 × 8 × 8 unit cells, each containing 8 atoms, although only 18 of the 4096 atoms are shown in Fig. 1. Periodic boundary conditions were applied. The simulations were carried out using the empirical potential proposed by Shimojo et al. [15]. The coefficients describing the potential for InSb were provided by Rino et al. [16]. These potentials have previously yielded correct cohesive energy and elastic constants. They have also been used successfully to model pressure-induced phase transitions in InSb  [17]. The MD simulation is classical and does not take quantum vibrational effects into account.
[20]. It is close to one of the energy densities (0. 259 Ry per atom pair, yielding an electron temperature of 100 mRy) considered by Zijlstra et al. [5]. The scattered x rays were detected using an Andor iKon camera. A series of single-shot images of the 111 Bragg reflection were recorded. The temporal evolution of this reflection was determined by fitting the decrease in intensity along the temporal coordinate to a Gaussian function: IðtÞ ¼ Ið0Þe -ðt=τÞ 2 ; t > 0: ð8Þ Single shot recordings of the scattered x-ray intensity as function of time for temperatures of 500, 150, and 70 K are shown in Fig. 3. In order to reduce and estimate the statistical error, we collected data from a series of such shots, each on a pristine part of the sample. The number of shots per series is given in Table  I.
was used to time stamp and average the shots  [2]. The procedure consists of three steps. First, the single-shot data were fitted assuming the inertial model. The time at which the x-ray intensity was reduced by a factor two was then determined. Finally, this was used as a timing marker, and the data were shifted before summing the data from the single shots to calculate the average. Figure  5  shows the average displacement derived from our data. It can be seen that the data and the lines representing   1 )-(  5 ). For intensities below 30%, the noise gives rise to large errors in the calculated displacement. We have plotted these points with lower reliability in gray. inertial motion are in excellent agreement. This can be expected since the disordering time is in agreement with the inertial model.
Research Council (VR, Grant No. 2014-4732 ),  Stiftelsen Olle Engkvist byggmästare  (Grant No. 1041513 ), and the X- PROBE Marie Skłodowska-Curie ITN within Horizon 2020  (No. 637295 ) for financial support. The FemtoMAX beam line was funded by the  Knut and Alice Wallenberg Foundation  (KAW, No. 2010. 0098 ).
METHODOLOGY To determine the role of stratigraphical heterogeneity on conduit development and aquifer function, six karst caves were studied in detail. Additional evidence was obtained from six caves and conduits elsewhere in Normandy and southern England. For each cave, a detailed cave survey was produced, followed by geomorphological and morphometric analysis of the passage and sediments. The caves were surveyed using standard cave surveying methods, which involve measuring the distance, direction and inclination between successive survey stations within the cave using a DistoX2 laser range-finder that incorporates a digital compass and inclinometer. The heights and widths of the passages were also measured at each survey station. The raw survey data were computed using Compass software to create the georeferenced survey in a geographical information system (GIS) (ArcGIS 11.
This was done by identifying key marker beds and biostratigraphy. The stratigraphy is based on sections exposed along the Normandy coast, and from inland sections and shown on geological maps  (Quince & Bosence, 1991; Juignet and Breton, 1992; Van Lint et al. , 2003; Hoyez, 2008; Quesnel et al. , 2008; Lasseur et al. , 2009; Mortimore, 2011 Mortimore, , 2019;; Gale, 2019). Additional data was derived from more detailed, targeted geological mapping of the area and detailed section logging at key sites including within the caves. Petrographical analysis of 17 thin sections was used to integrate the existing geological map. Profiles of each cave were extracted from the cave survey data and projected onto high-resolution stratigraphical sections. Finally, we quantified the 3D geometry of the study caves using 14 key morphometric parameters  (Klimchouk, 2006; Piccini, 2011)  to establish a relationship between the cave geometry and Chalk stratigraphy (Table  1 ).
The data collected from the studied caves and coastal sections is used to identify the key stratigraphical inception horizons that guide groundwater flow in the aquifer. In our study, ~68% of the passage length in the studied caves were influenced by the middle Turonian Le Tilleul hardgrounds located at the top of Val-St-Nicolas Fm, the lower Coniacian Epivent and Bocage hardgrounds at the top of the St-Pierre-en-Port Fm, and the upper Coniacian Veulettes hardgrounds within the Veulettes Fm (Table  4 ). The Tilleul and Epivent hardgrounds are basin scale features than can be recognised across northern France, whilst other hardgrounds are more localised regional-scale horizons  (Hoyez, 2008). Sheet and semi-tabular flint bands influence the development of ~10% (0. 6 km) of the studied phreatic/ epiphreatic passage in the Normandy caves (Table  4 ).