dataesr/scientific-paragraphs-categorization huggingface dataset:
= eq. (  1 ) With λ750: maximal reflectance wavelength, and λ675: Chl a absorption wavelength Daily measurements were performed on each sample of culture to follow the growth kinetics (at the beginning of the growth phase and during three days, between 3 mL and 5 mL of cultures were sampled, and then 2 mL until the end of the growth). To extract and compare the growth parameters from the alternative techniques and the cell count approach, the Gompertz model  [33]  was used to fit the growth data (equation. 2) using MATLAB software. It consisted of a latency phase followed by an exponential phase and a stabilization of the curve at its maximum phase.
Data processing The Pearson product-moment correlation was carried out to test the positive correlation between the growth curves obtained with the cell count (Cells. mL -  ), fluorescence (F0) and reflectance (NDVI). Comparison of the growth rate and LED estimated from the three techniques for the selected species were performed using ANOVA when the data presented normal distribution or the Kruskal -Wallis test when the data distribution was not normal. It was systematically followed by the Tuckey post hoc test. The ANOVA was also performed on the growth rate (µmax), production (Px) and lipid rate (TLR) results to identify the strains with the highest performance. A multivariate correspondence analysis was performed on the FTIR ratio normalized by silica for lipids, protein and carbohydrates to assess the dispersion of the biochemical information across the screened species and to identify if strains could be classified according to this information.
This method was chosen since it analyzes binary, ordinal and nominal data without distributional assumptions (unlike traditional multivariate techniques) and also to preserve the categorical nature of the variables. The correspondence analysis provided a unique graphical display showing how the variable response categories were related  [40]. The Pearson product-moment correlation was used to test the correlation between the calculation methods using the FTIR spectra semi-quantitative information with the total lipid quantification reference method (the gravimetric approach). The Pearson product-moment correlation and the comparison of the growth parameters were carried out using SigmaStat 3. 1 software. The Past3 software was used for the correspondence analysis approach. All experiments were performed in triplicate.
Diatom strain characteristics Among the 68 screened strains, 33 were cultivated successfully and 36 did not grow (supplementary data 1). This information is reported in Tab. 3, (with the NCC reference, the sampling location, the cell size, the LED, the growth rate, the silica content, the DW and lipid rate). Globally the cell length varied from 58 ± 14 µm (Craspedostauros sp. day -1 and group C with 18 species (54%) had a growth rate below 0. 8 day -1 with a minimum at 0. 22 ± 0. 05 day -1. To ensure the standardization of the FTIR results for inter species comparison purposes, the total silica rate was evaluated for the 33 species. It showed a significant difference (ANOVA, p < 0. 01), where five species (i. e. 15% of the total number of strains) had a silica content of c. a. 40% of the dry weight with a maximum of 46.
Multivariate analysis of the FTIR spectra recorded on entire cells In order to assess the main differences in terms of biochemical composition among the 33 screened strains, a correspondence analysis approach using the lipid, protein and carbohydrate bands normalized to the silica amounts was performed. The resulting map (Fig. 4 ) is a classification of the data on two main dimensions. Dimension 1 represents 81% of the initial information and could be associated to the variation of the lipid composition of the assessed strains, ranging from the lowest to the highest amount of total lipids from right to left on the map. Two strains, Staurosira sp. NCC182 and Amphora sp. 2 NCC169 presented the highest amount in total lipids. Along dimension 2, representing 19% inertia, the strains associated to that dimension were mainly opposed on the basis of their protein and carbohydrate content.
Strain selection The selection of the strains exhibiting both high biomass productivity and high lipid rates, was performed using whole sample distribution based on the lipid rates as estimated by the FTIR approaches, gravimetry and strain biomass productivity as estimated by the fluorometry. On the boxplots summarizing this data (Fig. 6 ), the colored dots represent the species with the highest potential for biotechnology applications based on lipid molecules: Nitzschia alexandrina NCC33, Staurosira sp NCC182 and Opephora sp NCC366 presented a lipid ratio whatever the considered technique and productivity above the median. Amphora sp 2 NCC169 and Nitzschia sp 5 NCC109 were also identified with an above median lipid ratio, but with lower productivity. Both these species were finally selected for their high lipid rate, even though their productivity needs to be improved.
Acknowledgements This work is carried out in the frame of  SUDOKET  project and is co-funded by the  Interreg Sudoe Programme  through the  European Regional Development Fund (ERDF). The authors acknowledge them as well as the financial support of  Region Nouvelle Aquitaine  for subsidizing  BioMCP  project (Project-2017- 1R10209-13023 ). We also would like to thank  CNRS  for promoting the I2M Bordeaux -CICe exchanges in the framework of the  PICS PHASE-IR  project.
Acknowledgements Dr Paloma Almodovar  is acknowledged for her fruitful discussions about CRM and SEM characterization. The authors also acknowledge the  Spanish Ministry of Industry, Economy and Competitiveness  for nancing the project  MAT2015-65445-C2-1-R ,  MAT2017-86450-C4-1-R ,  MAT2015-67557-C2-1-P , by the  Comunidad de Madrid   S2013/MIT-2850 NANOFRONTMAG  and  H2020   AMPHIBIAN  Project ID:  720853. The authors are also grateful to the  BM25-SpLine  staff for their valuable technical support beyond their duties and for the nancial support from the  Spanish Ministry of Science, Innovation and Universities (MICIU)  and The  Spanish National Research Council (CSIC)  under Grant No. PIE 2010-6OE-013 , The  ESRF -The European Synchrotron, MICIU  and  CSIC  are acknowledged for provision of synchrotron radiation.
Data The reference load profile comes from a large dataset of typical U. S. household load profiles (EERE, 2018). These are simulated using local weather conditions and the characteristics of an average local household. These load profiles are normalized, meaning that they do not exactly reflect the exact variability found in a real individual load profile, but are representative of the average consumption of an individual consumer. For the purpose of our analysis, this typical normalized load profile is sufficient. Further analysis, detailing the possible impacts for a wider set of load profiles, could be conducted in the future. See  Azarova et al. (2018)  for an approach using a large sample of real Austrian household load profiles. We use the city of Chicago, with its humid continental climate, as a reference because its annual load profile includes both a summer and a winter peak, and its shape is representative of that of many North American, European, and Asian cities.
CuxO/TiO2 Characterization: Thickness, TEM, CA, spectral absorption and XPS Thicknesses of the TiO2, CuxO and CuxO/TiO2 thin films were measured by profilometry (Alphastep-500, Tencor, USA) after deposition on Si-wafers. To be able to perform the transmission electron microscopy (TEM), the PES fabrics were embedded in epoxy resin (Embed 812) and cross-sectioned with an ultra-microtome (Ultracut E) up to thin layers of 80-100 nm thick. These thin layers were then placing on TEM holey carbon grid in order to image the samples. The Spot size was set to 5 µm, dwell time 50 µs and the real time of 600 s. The contact angles (CA) of TiO2, CuxO/TiO2 were determined by the sessile drop method on a Data Physics OCA 35 unit. Diffuse reflectance spectroscopy (DRS) of TiO2, CuxO and CuxO/TiO2 was carried out a Perkin Elmer Lambda 900 UV-VIS-NIR spectrometer within the wavelength range of 250-750 nm.
The background was subtracted using an unsputtered PES fabric. Optical spectra were analyzed by the Tauc's method. An AXIS NOVA photoelectron spectrometer (Kratos Analytical, Manchester, UK) provided for with monochromatic AlKa (hʋ=1486. 6 eV) anode. Binding energies (BE) were calibrated against the standard C1s binding energy at 284. 6 eV  [27, 28]. A Multipak (version 9 software) using 70:30 Gaussian: Lorentzian peak shape and a Shirley background function were used for Spectra deconvolution as previously reported  [28, 29, 36, 37].
XAS data collection and analysis. The preparation method of the XAS electrodes can be referred to our previous work  (26, 27). The ex situ XAS experiments were conducted at room temperature in a previously described flow half-cell. The data at the Fe K-edge of the samples were collected in the transmission mode at the beamline 6-BM of the National Synchrotron Light Source (NSLS) II, Brookhaven National Laboratory (BNL). Typical experimental procedures were utilized with details provided in our previous work  (26, 27).
Acknowledgements This work was supported by the  US Department of Energy  under award number  DE-EE0008416  and  DE-EE0008075. The authors acknowledge the support from the  DOE Energy Efficiency and Renewable Energy Fuel Cell Technologies Office  ( DOE-EERE-FCTO) ElectroCat consortium. This research used beamline 6-BM, 7-BM and 8-ID (ISS) of the National Synchrotron Light Source II, a  U. S. Department of Energy (DOE) Office of Science User Facility  operated for the  DOE Office of Science by Brookhaven National Laboratory  under Contract No. DE-SC0012704. Argonne is a  U. S. Department of Energy Office of Science Laboratory  operated under Contract No. DE-AC02-06CH11357  by  UChicago Argonne, LLC.
The injection volume was 5 μL. Complete calibration curves were performed at the beginning and at the end of the sample set, the mean slope value of these curves was used for DCF quantification. The limit of detection (LOD) and the limit of quantification (LOQ) were 0. 180 and 0. 547 mg L -1 , respectively. For the detection of DCF degradation intermediates, the full scan (FS) mode was performed, in a scan range of 60 to 500 m/z. Spectra were recorded with an average of 20-25 scans under identical experimental conditions. The reference full scan spectrum is presented in Fig. 1. The structural identity of each biodegradation product was performed with the LC-MS/MS fragmentation analysis. The analytical device was controlled by Micromass MassLynx 4. 1 software.
Statistical analysis Mean and standard deviation values of the three biological replicates were calculated for each treatment and for the control. In all cases, data are given as mean value with standard deviation. Two-way ANOVA was used to analyze the effect of DCF tested concentrations and culture duration on growth and photosynthetic activities and one-way ANOVA was performed to determine the significant differences between removal efficiencies of studied species among DCF tested concentrations, using IBM SPSS Statistic 21. 0. 0 software. A Pvalue < 0. 05 was considered statistically significant. When significant differences were observed, means were compared using the Post Hoc test for Two-way ANOVA analysis at a level of significance of 0. 05 (P<0. 05). For the DCF removal efficiencies, the significance of differences between the control and treated samples was analyzed by the one-way ANOVA analysis using the multiple-range Duncan test at the same level of significance (P<0.
Acknowledgements The Serbian authors acknowledge with thanks the financial support of the  Ministry of Education, Science and Technological Development, Republic of Serbia  through the Projects III 45019, III  45005. A. B. is grateful to the program " Start up for science 2016 " organized by the  Centre  for leadership development, Serbia that helped her to conduct herein studies. The Romanian authors acknowledge with thanks the partial financial support of this work by  UEFISCDI  under the contract  ID 304/2011.
The [660] reflection of three Ge(110) spherically bent crystal analyzers (a bending radius of 1 m) from the multicrystal spectrometer installed at BM16 was used to select the La1 (L 3 -M 5 ) fluorescence line at 9442 eV. The total energy resolution of the HERFD-XANES data was estimated to be approximately 0. 7 eV. Energy calibration of the incoming radiation was performed prior to the measurements by recording the L 3 -edge transmission spectrum of Pt foil and assigning the maximum of the first derivative peak to 11564 eV. XAS data were analysed using the HORAE package, a graphical interface to the AUTOBK and IFEFFIT code. 46 The XANES and EXAFS spectra were obtained after performing respective standard procedures for pre-edge subtraction, normalization, polynomial removal and wave vector conversion.
Acknowledgements This work was supported by the  King Abdullah University of Science and Technology (KAUST). We warmly acknowledge the help of  KAUST Core labs , and the  Supercomputing Laboratory at KAUST  for the CPU time attributed to this research work. The  FAME-UHD  project is financially supported by the  French ''large loan'' EquipEx (EcoX ,  ANR-10-EQPX-27-01 ), the  CEA-CNRS CRG consortium  and the  INSU CNRS  institute.
7 mg and 0. 8 mg of purified protein per liter of R. sphaeroides culture for the K56M and K56H, respectively, against 3 mg for the wild-type enzyme. Induced Coupled Plasma -Mass Spectrometry (ICP-MS) experiments provides an estimate of the metal content in the purified NapAB samples. For both mutants, we found a relative metal content of around 1 Mo atom to 6 Fe atoms as usually found for the wild-type enzyme (table  1 ). Nitrate reduction activity was assayed spectrophotometrically for the wild-type NapAB enzyme and K56H and K56M mutants (tables 1 and SI. 1). Specific nitrate reductase activity of the wild-type enzyme in the presence of reduced benzyl viologen serving as electron donor produced a Vmax value of 24 ± 4 µmol. min -. mg -  and a Km for nitrate of 250 µM at pH7, data which are comparable to those previously obtained in the presence of reduced methyl viologen (25 µmol.
Enzyme concentration was 80µM for wild-type enzyme, 65 µM and 70 µM for the K56M and K56H mutants, respectively. The reduced state of the sample was obtained by adding a small volume of a concentrated and buffered dithionite solution at a final concentration of 2. 5mM. Experimental parameters: microwave frequency 9. 475 GHz, temperature 15 K (A, C) and 55 K (B), modulation amplitude 2 mT (A), 0. 2 mT (B) and 1mT (C), microwave power 4 mW (A), 1 mW (B, C), number of scans averaged 1 (A), 9-64 (B), 1-4 (C). The experimental traces were simulated by using the EasySpin software package  [43]  with the parameters given in table  2. Table  2 : g-and A-parameters for the c-type hemes, the [4Fe-4S] 1+ and the Mo(V) "high g resting" species in wild-type, K56M and K56H NapAB enzymes from R. sphaeroides.
The given parameters are those determined in the simulations of the EPR spectra by using the EasySpin software package  [43]. For the hemes and the [4Fe-4S] 1+ signals, g-strain values are indicated in brackets. For the Mo(V) "high g resting" signal, linewidths were simulated using a H-strain of 0. 30 mT. Splitting of the Mo(V) signal corresponds to hyperfine interaction with two nonexchangeable I = 1/2 nuclei attributed to the β-methylene protons of the cysteine that binds the molybdenum ion  [44]. nd: not determined. Enzyme concentration was 60µM for the wild-type enzyme and 70 µM for both the K56M and K56H mutants. Protein samples were reduced by adding small aliquots of a concentrated buffered dithionite solution to a final dithionite concentration of 2. 5 mM.
Acknowledgements The authors are grateful to the  EPR  facilities available at the national  EPR network RENARD  ( IR CNRS 3443 ) and the  Aix-Marseille University  EPR center. This work was funded by the  CNRS ,  Aix-Marseille Université , the  A*Midex foundation of Aix-Marseille University  (project  MicrobioE , grant number  ANR-11-IDEX-0001-02 ) and the  Agence Nationale de la Recherche  ( MOLYERE  project, grant number  16-CE29-0010-01 ). We thank  Maxime Rollo  for his help in the analysis of iron-sulfur center relaxation properties.
Acknowledgements The work performed at  Florida State University  was supported by the  Petroleum Research Fund of the American Chemical Society  (award  59251-ND10 ) and in part by the  U. S. National Science Foundation  (award  DMR-1507233 ). The work performed at the  International Iberian Nanotechnology Laboratory  was partially supported by the  European Union 's  Horizon 2020 research and innovation program  through the  CritCat Project under Grant  Agreement No. 686053 , and by  FROnTHERA  ( NORTE-01-0145-FEDER-000023 ) project conanced by  European Union Funds , through Portuguese  NORTE 2020 program. The EELS studies were performed with the nancial support from the  Agence Nationale de la Recherche  in the framework of the " Investissements d'avenir" program  with the reference  ANR-11-EQPX-0020.
Acknowledgments The authors acknowledge the financial support from the  Algerian  Project  CNEPRU  n°  B00L02UN150120130025. Computing facilities were provided by  ASELKAM Computer Center (UMMTO, Algeria ). The authors are grateful to  GENCI-IDRIS  and  GENCI-CINES  for an allocation of computing time (Grant No. 2017-2018-080649 ). The  COST CM-1006 action  is also acknowledged. This paper is dedicated to  Dr. Jean-François Halet ,  CNRS Director of Research at the 'Institut des Sciences Chimiques de Rennes, France ', on the occasion of his 60 th birthday.
, 2005). Therefore, the weight of the energy price in the determination of competitiveness is likely to differ between sectors. Using energy prices at the sector level allows to account for these differences by estimating models with industry specific parameters. This paper contributes by building a dataset of industrial energy prices indices with considerably improved coverage than previous studies. While energy prices for electricity generation and households are readily available, energy prices faced by industrial sectors are harder to obtain. For most OECD countries, internationally comparable industrial energy prices are often published only at the country level (averaged across all industrial sectors), and with frequently missing data points. 5 Our original dataset covers 48 countries for the period 1995 to 2015, covering 12 industry sectors (mostly in manufacturing).
Exchange rate assumptions are important for cross-country comparison of energy prices particularly when including developing countries. Lastly, the dataset is made available for download with this article, and is designed for flexible use e. g. categorical variables are created to enable discarding observations that have been imputed using different techniques. The resulting energy price index reveals a number of insights including the following: (i) across countries and over 20 years, the energy price gaps widened in real terms, the highest price being six times bigger than the lowest in 2015, (ii) for cross-sectional variation, the cross-country variation matter considerably more than the cross-sector variation (iii) for time-series variation, changes to fuel prices matters more than changes to fuel composition (iv) policies and taxation are a major source of variation in the energy price between countries.
In addition, we illustrate how the energy price index can be used in empirical analysis and find that there is substantial heterogeneity across sectors regarding the relationships between energy price and energy intensity as measured by energy expenditure as share of gross value added. Finally, we compute the energy price increases due to a hypothetical carbon price and show that they vary importantly between and within countries. This paper is structured as follows. Section 2 describes the methodology, and Section 3 describes the data employed in the construction of the indices and discusses the strategies used to tackle missing data issues. Section 4 provides some stylized facts that emerge from the energy price data. Section 5 illustrates how the energy price index can be used in the assessment of competitiveness impacts of energy and carbon prices.
7 In panel data analysis, the fuel substitution embedded in the VEPL presents an endogeneity concern as it also captures firms' responses to prices via changes in their energy mix choices. We therefore also construct an alternative index where weights are fixed over time, called the Fixed weight Energy Price Index (FEPI). 8 By construction, the FEPI is a good instrument in such analyses. It captures the variation in fuel prices alone, including policies and taxation, and switches off the source of price variation that is endogenously related to the technological choices of the firm. The key features of the VEPL and FEPI are summarized in Table  1  and the choice of methodology is discussed in the context of the price indices literature in Appendix A. 1. We also provide some guidelines about the interpretation and applicability of the two energy price series.
All variables included in the database are described in the codebook (Table  9  in Appendix A. 11). The basic methodology (i. e. a weighted average price) is not a new idea 9 , but compared to previous literature, our published index covers a broader set of countries, sectors and years, partly due to the way we deal with missing data as explained below. Furthermore, we provide a readily available instrument (FEPI) that takes care of some part of the endogeneity problem as well as some measurement error when combined with fixed effects. The indices are also constructed at the aggregate manufacturing level, and at the industry level (i. e. manufacturing sectors + mining and quarrying + construction, see.
Fuel price data The primary source for price data is the IEA Energy End-Use Prices database, and specifically the prices for the industrial sector (IEA, 2012, 2016b). The data points are 12-month averages and available in national currency per tonne of oil equivalent (toe). This represents the final industrial energy prices including taxes paid by industry for different fuels, and excluding VAT and recoverable taxes and levies. 17 To enhance international comparability of the data, the IEA uses a questionnaire, which for 16 See Appendix A. 4 17 The IEA defines this as "the average of amounts paid for the industrial and manufacturing sectors" and "include transport costs to the consumer; are prices actually paid (i. e. net of rebates) and; include taxes which have to be paid by the consumer as part of the transaction and which are not refundable.
This excludes value added tax (VAT) paid in many European countries by industry (including electric power stations) and commercial end-users for all goods and services OECD Member countries are filled by official bodies such as national statistics offices. 18 In non-OECD countries, the data are collected directly from government and industry contacts and from national publications. We deem this to be the most suited publicly-available data source for internationally comparable industrial energy prices covering a large number of countries. Yet there is room for measurement error. Microdata analysis shows that within-sector variations in energy prices can be considerable for example due to quantity discounts. Many countries also offer tax exemptions and other subsidies to specific users including energy intensive ones, but this is not consistently reflected accurately in official data (European Commission (2014a)   and OECD (2013) ).