dataesr/scientific-paragraphs-categorization huggingface dataset:
Data limitations and issues The program suffered from some instrument failures. ADCPs from F3B09&10 during leg 3 suffered from a battery failure (F3B09) and from a memory card failure (F3B10) resulting in data coverage of only 48% (slightly less than 3 months) and 17% (about 1 month), respectively. Two SBE19 CTDs (#1310 and #1312) got their electronic casing flooded resulting in a complete loss of data on F3B04, leg 2 (SBE19#1310) and in partial losses (small leak) on F3B03, leg 2, and on F3B07, leg 3&4 (SBE#1312, temperature and salinity data corrupted). The DOGIS SUTRON weather station suffered from a solar panel failure during leg 1 resulting in a reduced coverage of 92% (a loss of about 12 days) and from a wind sensor failure during leg 3 (%56 coverage, a loss of about 33 days). DOGIS U30 weather station suffered from barometer issues during leg 1, 2 and 3 reducing coverages to 96%, 83% and 87%.
QA/QC and data processing methods Data were processed and quality checked similarly for all the instruments, that is: 1) raw data were first converted to the most convenient format known/available to the authors 2) time stamp and all variables of interest were extracted from the raw data and meta-data were associated to the dataset (i. e. station ID, geographical coordinates, deployment and recovery date and time and instrumentation ID) 3) using deployment and recovery time, 'out-of-water' data were removed were the most often used as a reference since their pressure sensor were systematically 'zeroed' prior to deploy and that their clock did not drift more than a few minutes per deployment. U20s and RBRs were usually used as a secondary reference or as a primary one when no ADCP were available on the same line (e.
g. in leg 1&2). SBE19 units were the most affected by clock-drift and depth offset. A few units (SBE19 as well as UTBI) were also found to have been setup in local time instead of UTC, mistakenly. 5) 'out-of-range' data were removed using automatic filters following the criteria shown in Table  2. ADCP criteria was largely based on the manufacturer recommendations with current speed less than 0 (bad values are actually logged as -32768; see T-RDI documentation #P/N 957-6156-00, p147), Percent Good (PG) less than 25 (T-RDI documentation #P/N 957-6156-00, p150) and instrument tilt over 15 degree from the vertical (T-RDI documentation #P/N 957-6150-00, p17) used to remove bad data. Instrument vertical tilt was calculated using the pitch and roll records (see below for details). In addition to those data quality filters, a 'surface rejection' filter was applied as a percentage of the range to sea-surface (or sea-bottom for the downward looking F3B09&10) usually equal to 10% (i.
UTBI depth calculation 310 Since our thermistors (UTBI) did not have embedded pressure sensors, the depth of their temperature records needed to be estimated. This calculation was done in three steps, increasing the accuracy of the estimate at each step: 1) Once a site depth was accurately determined, mean depth of each UTBI was determined using the mooring diagrams providing with the distance from sea-bottom of each instrument. Mean depth (with respect to Mean Sea Level, MSL) was then determined as: site depthheight above sea bottom. 315 2) 'tidal depth', i. e. depth varying due to the tide alone, was determined using the results of tidal analyses made on the instruments equipped with a pressure sensor (i. e. ADCP, CTD and U20). One reference per mooring line was used, typically the instrument located the closest to the top of the mooring having the highest data coverage so that the overall mooring tilt was best approximated (CTD or U20).
In-situ comparisons (e. g. CTD profile vs. mooring data) A CTD cast was performed after each mooring deployment and just before mooring recovery (see Figure  6  as example). Thus, a total of 52 casts were available for in-situ checks (F3B03 and F3B04 pre-recovery casts were missed). The primary goal of those checks were to assess the performance of the moored UTBI lines (Temperature) and moored CTDs (Salinity). Overall, a mean difference of 0. 12 o C and associated mean standard deviation of 0. 11 o C was found between the CTD profiles and UBTI observations and an overall mean salinity difference of 0. 07 and mean standard deviation of 0. 03 between the CTD profiles and moored CTD was obtained. which can be anywhere from 20 to about 25 degree (see Table  3 ), current speed calculation and bin mapping will become biased; profiles data will then likely be unrecoverable.
An illustration of this issue and potential recoverable data is presented in Figure  7. Top 50 m (not affected by over-tilted position) and bottom (over tilted from December 7th) ADCP profiles are plotted together, showing the effects of the tilt on current direction and current vertical component w but not on current speed and acoustic backscatter. In our quality control process, a 'combined' tilt angle, i. e. combination of pitch and roll angles, were used to filter unreliable current direction and earth coordinates velocities (i. e. u, v and w). This 'combined' tilt was calculated as follow: tilt = acos(cos(pitch). *cos(roll)) This rejection is somehow conservative since this 'combined' angle is always larger than the pitch and roll taken individually. In addition to this automatic filter, bench tests were performed on each of our ADCPs to determine their maximum pitch and roll angles measurable by placing each unit horizontally on a table on each direction, i.
e. Beam 1-2 and Beam 3-4 axes which helped us to further assess the quality of our data (Table  3 ). Five timeseries were affected by this issue in total: F3B01 leg 2 (bottom unit), F3B01 leg 3, F3B05 leg 3&4 and F3B07 leg 3. Being tilted near both pitch and roll limits, the latter was corrupted beyond repair and nothing could be saved from it. The other four were generally severely tilted, but below the limits, on one side 'only'; current speed and backscatter profiles were saved from those timeseries. depth, temperature etc. ). UTBI timeseries were bundled together into one folder per mooring line and, thus, 1 processed file per timeseries. CTD profiles were bundle-up per surveys and formatted as tab-delimited ASCII ODV4 files (Schlitzer, 2019). Earth System Science Data Discussions Preprint.
NetCDF files were created under the MATLAB environment and tested using the NetCDF utilities (ncdump from unidata), python (with xarray and panda libraries) and the Interactive Data Language (IDL) environments. Care was taken to export as much data from the raw as possible (e. g. ADCP correlation magnitudes) but are provided as 'processed', that is, bad data flagged by the QA/QC (Quality Assessment/Quality Control) process described below were replaced by NaN (Not a Number) values.
Conclusion We present an oceanographic dataset collected in a subpolar, mid-latitude, broad fjord. The data collection was centered on the deployment and recovery of oceanographic moorings and a few land-based stations collecting physical parameters such as: water temperature and salinity, ocean currents, wind speed and direction and tide. The main goal of this observation program was to serve as a base to further study the main physical processes affecting this embayment which are likely common to other wide stratified fjords. To our knowledge, very few embayments alike, i. e. broad fjords, have such comprehensive observations combining numerous and continuous in-situ sampling points. Several bays in the region have been well explored in the past but their continuous observation via moorings rarely extended more than a few months during the spring to fall seasons, thus not offering a complete seasonal picture (e.
g. Yao 1986 , de Young and Sanderson 1995 , Hart et al. 1999 , Schillinger et al. 2000, Tittensor et al. 2002a&b ). Abroad, with the increasing interest in polar regions and their importance in climate, many recent studies relied on significant datasets collected in broad fjords (e. g. Straneo et al. 2010 , Jackson et al. 2014 , Inall et al. 2015 , Merrifield et al. 2018). Generally, and most likely due to extremely challenging technical constrains (e. g. massive glacial ice), those datasets remain scarce and limited to a few points and/or few months observation, however. By combining a relatively large number of observation points (up to 21 moorings during leg 3), high vertical resolution in both thermal stratification (2-10 m) and ocean currents (1-3 m) as well as duration (2-years), we believe that this dataset should be comprehensive enough to study a wide variety of processes; making it of particular interest to be shared.
Electrochemical Properties and Oxygen Redox To understand the oxygen redox reaction in the samples, X-ray absorption spectroscopy (XAS) measurements were carried out. The Ni K-edge XANES data during first cycle (Figure  S9 , Supporting Information) shows that the Ni redox reaction from 2+ to 4+ in the samples can contribute up to ≈125 mAh g -1 to the obtained capacity. 2b,g] As a result, the oxygen redox reaction in the IC sample can contribute the additional capacity, ≈175 mAh g -1 to the obtained discharge capacity indicating substantial improvement of reversible oxygen redox contribution compared to the NIC sample, ≈125mAh g -1 from the oxygen redox reaction (Figure  3a ). Improved oxygen redox reaction in the IC sample is clearly supported by the integrated intensity of the pre-edge region below ≈534 eV in the O K-edge SXAS (soft X-ray absorption spectroscopy) spectra (Figure  S10  and Table  S6 , Supporting Information).
The pulverized powder was re-pelletized. 4) The pellets were reannealed for 5 h in air at 800 °C with ramping temperature 5 °C min -1 (sample name: inter-diffused cation, IC) then quenched to room temperature (RT). Alternatively, the pellet was reannealed at 800 °C for 5 h in air, then cooled naturally to RT (sample name: not inter-diffused cation, NIC). Detailed synthesis processes are explained in Supporting Information. Material Characterizations: The neutron diffraction measurements were performed at the Australian Centre for Neutron Scattering. The wavelength was 1. 5340 Å and the scan range was 10-150°in increments of 0. 05. All data were collected at RT. The synchrotron X-ray diffraction measurements were performed on beamline 9B-HRPD at Pohang Accelerator Laboratory (PAL), Pohang, Korea.
The incident X-rays were vertically collimated by a mirror, and then monochromated to the wavelength of 1. 4970 Å by a double-crystal Si (111) monochromator. The datasets were collected in the range of 10°≤ 2 ≤ 130°with a step size of 0. 02°(2 range). Rietveld refinements for the lattice parameters of each sample and the quantity of impurities were determined using Full Proof software. High-resolution transmission electron microscopy (TEM) analysis was conducted on a JOEL JEM-2200FS microscope fitted with a LaB6 filament at an acceleration voltage of 200 kV. Nuclear Magnetic Resonance Measurements: 6 Li and 7 Li MAS NMR measurements were performed at RT on a Bruker Avance-200 spectrometer (B0 = 4. 7 T, Larmor frequency  0 = 29. 45 and 77. 78 MHz for 6 Li and  7  Li, respectively). The 6 Li isotope was a spin-1 nucleus and had the smallest quadrupole moment for any nucleus and it could be considered as a spin-1/2 nucleus.
e. , Mn K-edge = 6539 eV, and Ni K-edge = 8333 eV). Data pre-processing operations such as deglitching, energy calibration, normalization, and least square fitting with theory were performed by using IFEFFIT which used the FEFF code. Soft XAS spectra of O K-edge were recorded at beamline 8A1-SPEM and 10D-XAS KIST of PLS-II in total electron yield (TEY) mode and fluorescence yield (FY) mode under high vacuum condition with a base pressure of 3 × 10 -10 Torr. The spectral energy resolution was 0. 1 eV and the monochromator absorption features were normalized by dividing the detected signals (I 1 ) by the photoemission current, I 0 of a gold mesh placed in the incident beam. All the absorption spectroscopy data were measured at RT. Data pre-processing operations such as deglitching, energy calibration, normalization, and least square fitting with theory were performed by using WINXAS program with two or three Gaussian functions and one Arc tangent function.
All cells were tested at room temperature. For ex situ experiments, the device in glovebox was disassembled, taking care not to short-circuit it. And then the electrodes were rinsed with dimethylcarbonate to remove residual electrolyte salt and allowed them to dry. The electrodes were covered for ex situ study with teflon tape or scotch tape and stored in the glovebox until the experiment was carried out. Density Functional Theory Calculations: Phase stability and voltage profiles of Li 1+2x Mn 0. 5 Ni 0. 5-x O 2 and Li 2-2y MnNi y O 3 (x, y < 1) were predicted based on total energies computed using the Vienna Ab initio Simulation Package (VASP) and the Perdew-Burke-Ernzerhof (PBE) functional in the generalized gradient approximation (GGA) and projector-augmented wave method. The on-site interaction was corrected by the Hubbard U parameters of 6.
Acknowledgements This research was supported by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF)  funded by the  Ministry of Science, ICT and Future Planning  ( NRF-2017M3A7B8065394  and  NRF-2019R1A2C2007933 ). This research was supported by the  Brain Korea 21   PLUS Project for Center for Creative Industrial Materials  ( F14SN02D1707 ) and a  POSTECH Basic Science Research Institute Grant. Part of the work was performed under the auspices of the DOE by the  Lawrence Livermore National Laboratory  under Contract  DE-AC52-07NA27344. The authors gratefully thank  Prof. Hong Li  for his insightful discussions.
Results Identification and functional characterization of IbHCT. BAHD acyltransferases have been previously shown to be involved in the synthesis of 3,5-DiCQA in Coffea canephora and Solanum lycopersicum. Therefore, our investigation first began by focusing on the identification of orthologous genes in I. batatas. Using the amino acid sequences of HCT from robusta coffee (ID: ABO47805) and tomato SlHQT (ID: Q70G32) as queries, a tBLASTn search was conducted on the GenBank Database. We focused on a putative IbHCT (protein ID: BAJ14794). This protein shares 85% amino acid identity with the HCT from C. canephora that was described to be involved in the synthesis of 3,5-DiCQA. This enzyme also shares 84% with an HCT from Nicotiana tabacum (ID: CAD47830) and 82% with that of Cynara cardunculus (ID: AAZ80046).
The protein contains the HXXXD and DFGWG motifs characteristic of the acyltransferases of the BAHD family (Supplementary Fig. 1 ). Based on the sequence available, the coding sequence was amplified from RNA extracted from I. batatas roots. The resulting sequence shows six different amino acids compared to the registered sequence. Since these amino acid mismatches were reproducible through several cloning experiments and were localized in nonconserved regions, this sequence was used for the following experiments (Supplementary Fig. 1 ). Since an HQT has been described for the same activity in tomato, we also cloned a gene encoding a putative IbHQT (Protein ID: BAA87043) for which the sequence was already available in public databases. To perform functional characterization of this enzyme, it was expressed in E.
batatas roots grown under nitrogen depletion conditions. Third, the molecular mass of the enzyme (50 kDa) does not match the value of the chlorogenic acid:chlorogenate caffeoyl transferase described previously by Villegas et al. 31. Therefore, another strategy was initiated in an attempt to identify additional enzymes involved in the synthesis of 3,5-DiCQA. Identification and functional characterization of IbICS cDNA library construction. Little genetic information concerning I. batatas was available in public databases, which led us to generate a RNA-seq library. Total RNA was extracted from the roots of plants deprived of nitrogen. As mentioned above, in roots of plants grown under nitrogen-deprived conditions, the amounts of 3,5-DiCQA doubled compared to the control conditions. RNAs were extracted from roots of these plants to construct a 454-based de novo RNA-seq library (ID: PRJNA647243).
This analysis provided several hundred peptide sequences (Table  1 , and Supplementary Table  1 ) that have been further identified and assigned to 25 (fragment #3) up to 64 (fragment #5) homologous proteins present in public databases. Identification of a full-length cDNA encoding IbICS. The peptides identified by UHPLC MS/MS were used in a tBLASTn search of the de novo normalized RNA-seq library obtained from I. batatas roots to subsequently identify the corresponding coding sequences. A total of 24 proteins were identified in the four selected gel samples (Supplementary Table  1 ), and among these proteins, a sequence matching with a predicted Ipomoea nil GDSL esterase/lipase At1g28590-like (XM_019313071. 1) was detected in fragments #3, #4 and #5. This result led us to identify IbICS, a 1158 bp coding sequence (CDS) (Genbank ID: MT291823) (Supplementary Fig.
In this study, an HCT isolated from I. batatas was shown to be able to synthesize 3,5-DiCQA using chlorogenic acid and caffeoyl CoA as substrates in addition to its main catalytic activity towards 5-CQA production. Such enzymatic activity has already been demonstrated for an HCT isolated from coffee. The synthesis of GDSL involved in specialized metabolism GDSL involved in plant defense GDSL involved in plant development and morphogenesis No defined Fig. 4  Phylogenetic tree of GDSL lipase/esterase protein sequences available in public databases. The tree was constructed using the neighbour joining method and adapted from Chepyshko et al. 35. 3,5-DiCQA was found to be optimal at pH 6, while the synthesis of chlorogenic acid was optimal at pH 7 (not shown). This difference could be explained by the fact that the activity of the enzyme may change depending on the cell compartment.
1% formic acid in water) and solvent B (0. 1% formic acid in acetonitrile). Chromatographic separation was achieved using a 180 min linear gradient from 0 to 50% solvent B. Processing, deconvolution and peptide detection were performed using Data Analysis software (Protein Lynx global server, Waters). Bioinformatic analysis. The FASTA file provided by MWG Eurofins Operon (Ebersberg, Germany) was imported as a library into BioEdit software (http://www. mbio. ncsu. edu/bioedit/bioedit. html). The peptide sequences obtained by MS analysis were compared with the RNA data available in the cDNA library using the Basic Local Alignment Search Tool (tBLASTn) to identify the corresponding coding sequences. A global analysis of the RNA-seq database was performed with the Blast2GO platform. The prediction of putative N-terminal signal peptide (SP) was performed using SignalP 4.
0 to 9. 0 with a single 5-CQA concentration of 10 mM. To evaluate the best substrate concentration, the 5-CQA concentration was modulated from 0 to 30 mM. The reactions were stopped by adding 150 µL absolute ethanol, and the solution was mixed and centrifuged at 14,000×g for 10 min at 4 °C. The supernatant was analyzed by UHPLC-MS. Reactions were run in triplicate. K m value was determined in triplicate by fitting Michaelis-Menten curves using SigmaPlot Software. Expression of IbICS in E. coli and S. cerevisiae. Expression in E. coli was realized in the BL21 Rosetta strain, and the culture was performed as for CcHCT described in Comino et al. 3. Expression in S. cerevisiae was performed in the WAT11 yeast strain  56  , as described for cytochrome P450s  57. Expression and in vivo bioconversion of 5-CQA to 3,5-DiCQA by recombinant IbICS produced by P.
Acknowledgements The authors would like to thank  Clément Charles  for his technical support. This work was financially supported by the " Bioprolor2" project (Région Grand-Est ) (to A. H. ) and by the " Impact Biomolecules" project of the "Lorraine Université d'Excellence" (Investissements d'avenir-ANR ) (to A. H. ). Experiments performed at  Charles Viollette Research Institute  were supported by an  ALIBIOTECH program  ( 2015-2020 ) obtained from the  CPER/FEDER Région Hauts-de-France. Marianne Delporte  and  Guillaume Legrand  were supported by doctoral fellowships from the doctoral school 104 SMRE.
The present work deals with the spatial consistency of two well-known databases of solar radiation received at ground level: the CAMS Radiation Service database version 3. 2, abbreviated as CAMS-Rad and the HelioClim-3 database version 5, abbreviated as HC3v5. Both databases are derived from satellite images. They are validated against 10 min means of irradiance for the period 2010-2018 recorded in a network of 26 ground stations in Germany operated by the Deutscher Wetterdienst (DWD). For the CAMS-Rad database, the correlation coefficient between ground measurements and estimates ranges between 0. 83 and 0. 92 for all sky conditions. The bias ranges from -41 and 32 W m -2 (-11 % and 10 % of the mean irradiance). The standard deviation ranges between 89 and 129 W m -2 (25 % and 39 %). For the HC3v5 database, the correlation coefficient ranges between 0.
90 and 0. 95. The bias and the standard deviation are comprised between -22 and 16 W m -2 (-6 % and 5 %), and between respectively 70 and 104 W m -2 (20 % and 31 %). For the CAMS Rad database, overestimation is observed in the South, and underestimation in the North with a faint tendency of the bias to increase from East to West. For the HC3v5 database, the bias is fairly homogeneous across Germany. For both databases, there is no noticeable spatial trend in the standard deviation.
There are a limited number of such high-quality devices. They can thus not address the need to assess E at any place and at any time. Other means of estimating E are exploited to compensate the lack of this type of instrument. Global meteorological reanalyses exist that go back several decades in time such as ERA-Interim, ERA5, MERRA, and MERRA-2  (Hersbach and Dee, 2016; Rienecker et al. , 2011; Molod et al. , 2015). However, several works have shown that the estimates of E from these reanalyses have significantly lower accuracy than those from satellite-based databases  (Bengulescu et al. , 2017; Boilley and Wald, 2015; Jones et al. , 2017; Trolliet et al. , 2018; Zhang et al. , 2016). In particular, these authors have reported that reanalyses often exhibit cloudy conditions when the actual conditions are cloud-free and vice versa.
Satellite images are less prone to this kind of error since they are based on a direct observation of the cloud coverage. They have been successfully used in the past decades  (Amillo Published by Copernicus Publications. et al. , 2014; Janjai et al. , 2009 Janjai et al. , , 2011;; Lefèvre et al. , 2014; Müller et al. , 2015). Several databases have been constructed from images acquired by the Meteosat series of satellites. They offer synoptic views of Europe, Africa and the Atlantic Ocean since 1983 with a spatial resolution of approximately 3 km at nadir and a temporal frequency of 30 min for the first generation of Meteosat satellites and 15 min for the second generation. This article deals with two of these satellitederived databases of E: the CAMS Radiation Service, abbreviated as CAMS-Rad and part of the Copernicus Atmosphere Monitoring Service (CAMS), and the HelioClim-3 version 5, abbreviated as HC3v5.