dataesr/scientific-paragraphs-categorization huggingface dataset:
In short (analogous for PPP prices): P U SD constant = P LCU nominal Def lator LCU * Def lator LCU 2010 ER LCU/U SD 2010. ( 3 ) For a few countries, inflation was extremely high in some years (e. g. Kazakhstan or Turkey). We include an inflation rate variable in the dataset, which allows excluding observations where inflation rates are high. The prices that go into the calculation of the VEPL and FEPI are thus in real terms and only capture price increases in the fuel basket relative to the general economy-wide price inflation. (including energy). In these cases VAT is refunded to the customer, usually in the form of a tax credit. Therefore, it is not included in the prices and taxes columns in the tables. "(IEA, 2012). Self-generation or consumption of by-products is not included as it is energy purchase data.
Missing data management in fuel prices The IEA energy price data have significant gaps. Where these cannot be filled by other data sources such as data from national statistics offices, we fill data gaps and increase the coverage of the VEPL and FEPI using a simple and transparent approach. Specifically, we look for other observed energy price series which are more complete. In the case of OECD countries, the IEA publishes an industrial real price index for each fuel type (oil, gas, coal, electricity) within the same database (IEA, 2012,   2016b), which tends to have less gaps than the fuel price series themselves (and also includes taxes, but no VAT as described in Footnote 17). If prices for coal in the UK, for example, are available for some years but missing for others, then we can extract the coal price growth rates over time from the real coal price index for the UK and apply them to the UK coal price data in order to fill the gaps.
Fuel use data for weights The fuel use data in toe is derived from the IEA World Energy Balances IEA (2016c) for all countries, reflecting consumption by fuel type at the sector level. 19 In order to combine it with the price data, all fuel sub-types are aggregated into four groups of consumption in toe: oil, gas, coal and electricity. 20 The final amounts of fuel type use per sector serve for the construction of the weights, where a specific weight is calculated as the energy input in toe of a certain fuel type as share of the total energy input in toe.
Within countries, fuel prices and taxes vary little across industrial sectors The VEPL is a weighted average of country-level fuel prices for industry, hence relies on the assumption that fuel prices do not vary significantly between industrial sectors within a country. Here we investigate if fuel taxes indeed vary within countries and across sectors, using a unique database collected by the OECD (  2013 ) on effective tax rates on different fuels at the sub-industrial sector level in OECD countries in 2012. This allows us to assess the variation in the tax component across industrial sectors. We calculate the coefficient of variation (CV) of the tax rates across sectors for each country and fuel (oil, coal, gas, electricity). Within-country variations in energy taxes across industrial sectors (dark  30  The R-squared is used as it is straightforward to interpret as the percentage of explained variation, but it should be noted that it is the correlation that is of interest here and not causality.
The number of observations (countries) is typically around 15-20, except for coal, where the number of observations is lower (6-8). 31 The high R-squared for coal can be attributed to the fact that there is not much differences in coal prices (non-tax component) across countries. bars in Figure  8 ) are found to be small compared to within-sector (across countries) variations (light bars). There is some within-country variation for coal and oil while for electricity and natural gas, similar tax rates tend to be applied across industrial sectors within a country. 32 The OECD (2013) database also allows us to perform an important robustness check. We can test if the VEPL is a good proxy for the observed variation in energy taxation across industrial sectors. We construct a "VEPL_tax" indicator where the sector level variation in energy prices are introduced using observed variations in sector level energy taxes.
40 Multiplying the carbon intensity of fuel mixes with carbon prices gives the resulting increase in the energy price. The variables FEPI_fw2010_tax_10 (. -50) in our dataset contain the hypothetical price increase (on a log scale) induced by a carbon tax of 10$/tCO 2 (five variables for 10-50$/tCO 2 in 10 unit steps), based on the FEPI_fw2010. 41 Therefore, the fuel use weights as well as the emission factor of electricity are taken from 2010 (the IPCC emission factors for other fuels are time-invariant). Note that the hypothetical carbon tax is additional to any already existing carbon tax. As the weights are fixed, any reduction in energy prices that carbon taxes may induce through fuel substitution is not accounted for. Hence this variable is suitable for estimating the short-term effects of carbon prices.
We found short-run correlations between sector level energy prices and energy intensity and energy costs, that vary substantially across sectors. More robust evidence, including on whether the positive effects outweigh the negative ones, would help in fine-tuning policies affecting energy prices. Better targeted policies would in turn lower the overall cost of achieving mitigation targets thus improving welfare outcomes. A number of recent studies have utilized the energy price index constructed in this paper to estimate the effect of asymmetric energy price on competitiveness outcomes including international trade, FDI and investments (Sato   and Dechezleprêtre, 2015; Garsous and Kozluk, 2017; Dlugosch and Kozluk, 2017). It is our hope that this dataset will contribute towards the development of the empirical literature.
In the case of constructing sector level energy prices, given that all quantities of energy in our data are measured in tonnes of oil equivalent (toe), we can use quantity weights, which are more direct and precise than using expenditure shares. For the frequently used arithmetic Lowe, Laspeyres and Paasche index, weighting by expenditure shares (ExpS) or quantities actually result in the same expression, once we compare two different points in time: Laspeyres index t,b=0 VEPL ∧ = (arithmetic) Lowe index t,b        = j P j t F j b j P j 0 F j b Paasche index t,b=t The anchor point b is the point in time of the fixed weight derived from either quantities or ExpS. The point in time of the anchor point is thus the only difference in these widely used indices and the Laspeyres and Paasche being special cases of the Lowe index where the anchor corresponds to the base or evaluation year respectively.
Paasche index t,b=t In the first equation, the expenditure share is taken and in the second equation, the quantity share is used to calculated the weighted geometric average. Taking logs of the second, quantity weighted version corresponds exactly to the change of the FEPI from year 0 to year t 46. Therefore, the FEPI can be interpreted as the log of the geometric Lowe, Laspeyres or Paasche index with quantity weights. Since our fuel use data is measured in a common unit toe, we can actually use this more precise version of weights and diverge from the usual geometric CPI indices that rely on expenditure shares. It is commonly noted that holding expenditure shares fixed corresponds to a perfect elasticity of substitution of one in these geometric indices, since the relative quantities are assumed to adjust perfectly to relative price changes so that the expenditure shares stay constant.
For some countries, we used data from the electricity generating sector from the IEA Energy End-Use Prices database to impute some data points. In particular, this refers to the coal price for Mexico, the growth rate in natural gas price for Indonesia and the growth rate in the coal price for Taiwan. In these instances, the levels and correlation of the prices with the industrial sectors are generally very high. For Taiwan, the growth rate in the industry low sulphur oil price is applied to the industry high sulphur oil price, both also being generally highly correlated. The added data points described here paragraph represent less than 6% of the sample. It is not clear a priori which approach is the better choice (i. e. country specific general energy or fuel specific but regional), so we apply a data driven approach which maximises out of sample prediction precision by country and choose the index accordingly on a per country basis.
A. 6 Threshold for ignoring missing prices for FEPI construction In some cases, it is not possible to construct a price index because prices are missing for a particular fuel type for all years, hence no growth rates from other dataset can be applied. If the fuel type with missing prices accounts for a small share of a sector's fuel consumption, then we consider it reasonable to construct the FEPI using the three remaining fuel types. Thus, the FEPI is allowed to be based on less than four fuel types, if the excluded fuel type represents less than 12% of the sectors' total fuel consumption. The 12% threshold was chosen by comparing the number of additional observations gained against the threshold level (Figure  10 ). As shown, thresholds beyond 12% do not significantly increase the number of observations recovered.
The gain in observations numbers is substantial compared to the version which requires all 4 fuel types -non-missing values increase from 69% to 85% (in the FEPI_fw2010 version). If the pattern of the ignored missing values are random there is limited harm in statistical inference and even if they are not random, a potential bias introduced is small due to the low weight. Our database contains also a version of the FEPI, where we do not allow this potential exclusion of fuels, which is called FEPI_allfuels. countries where both data are available, except for Norway). Notes: Shows the growth in observations by increasing the threshold (on the horizontal axis) for the FEPI_fw2010. After 12%, the additional growth in observations is less than 0. 5% for each percentage point increase in the threshold level.
indices available publicly. We gratefully acknowledge financial support from the  EU   Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 308481  (ENTRACTE), the  Economic and Social Research Centre Future Research Leaders  grant  ES/N016971/1 , the  Grantham Foundation  and the  ESRC  through the  Centre for Climate Change Economics and Policy , and the  Swiss National Science Foundation  project  Sinergia.
To capture the nanostructure with an average length scale of about 50 nm, slices of 5 nm were cut (voxel of 5 × 5 × 5 nm 3 ) and the thickness analyzed was of the order of 250 nm. Reconstruction of the 3D images were performed using ImageJ/Fiji (NIH, Bethesda, MD, USA) and Avizo software (Thermo Scientific, Waltham, MA, USA) as well as a homemade plugin  [26]. Nanoindentations were performed using a HysitronTI950 (Bruker, Eden Prairie, MN, USA) with a Berkovitch diamond tip. The surface topography and indents were imaged using the scanning-probe microscopy mode of the nanoindenter, which consisted of scanning the sample surface with the nanoindenter tip. Focused ion-beam (FIB) cutting of an indented zone of the material was also performed for detailed analysis and modeling. First, quasistatic measurements, with a maximum load of 900 and 10,000 µN, a loading rate of 100 µN/s, and a holding time of 2 s, were performed.
Funding: This work was supported by the  European ITN   Network  project " VitriMetTech " (grant No. FP7-PEOPLE-2013-ITN-607080 ) in the framework of the  Marie Sklodowska-Curie actions program. The program and works on nanoporous metals were initiated by  Alain Reza Yavary (SIMaP CNRS G-INP ). YC, AMJJ, and DB dedicate this article to his memory.
They found that the overall energy usage of the cold storage strategy was almost 4% lower than the non-storage conventional system. Moreover, Zhang et al. [11]  analyzed the effect of various types of soil, the tank's volume and its wall thickness on the water temperature, by presenting a model of an underground water tank insulated with polystyrene. The tank is considered as a heat source in winter and a source of refreshment in summer. Yumrutas and Unsal  [12]  studied the effect of soil type on the water temperature of an underground tank coupled with a heat pump. Ucar and Inalli  [13]  studied the transient temperature distribution of the soil surrounding a buried tank coupled to a solar collector using the Matlab code. Durao et al. [14]  also proposed an algorithm to optimize the combined solar-underground tanks system by varying the volume of the underground tank and the surface of the solar collector to ensure the heating requirements of a greenhouse are met.
Validation of the model of two water-tanks The simulations of the studied system are made using TRNSYS software which is designed to simulate the energy performances of dynamic systems (fig. 12 ). The essential types used for the simulation are: • " Type 38 " to the model of the two buried tanks connected in parallel: Type 38 (Plug-Flow Tank) models a stratified storage tank. It uses variable segments of fluid, which allows the model to simulate a particular zone of the tank using small segments without requiring a large total number of nodes or small time steps  [28]. The essential parameters used in the simulation include: 1. The total volume of the tank: 2000 liters. 2. The thermal conductivity of the tank (galvanized sheet): 48 W / m. K. 3. The thermal conductivity of the water: 0. 6 W / m.
Material The MLP and SPT analyses were based on five main empirical field studies undertaken in Bamako over the 1984-2015 period. The data gathered and used were intended to assess and analyse the energy consumption of Bamako households, their preferences and expectations, the functioning of the different energy supply chains, and the institutional context regulating this household energy sector. It benefited from the field work undertaken by the following projects (table 1), four out of five of which were coordinated by the authors' institution and two were implemented by the authors themselves. In addition, we collected statistical data from the National Directorate of Statistics and Informatics of Mali (DNSI) on LPG imports, energy prices, household revenue, household composition, etc. The main sources we used were:  (INSTAT, 2003 (INSTAT, , 2014 (INSTAT, , 2016a (INSTAT, , 2016b;; ODHD/LCPM, 2006; OEF, 2004).
[21] ISIS, Chilton, UK) and IN1-Lagrange (ILL, Grenoble, France). [22]  The IN1-Lagrange data is available at:h ttps://doi. ill. fr/10. 5291/ILL-DATA. 7-05-441. Diffuse reflectance infrared spectra were recorded by using aS pectra-Tech Collector with an environmental chamber fitted with KBr windows and either aD igilab FTS-60A or aB ruker Vertex70 FTIR spectrometer. T he computational studies are described in the Supporting Information.
Acknowledgements This work is based, in part, on experiments performed at the  IN1-Lagrange instrument at the Institut Laue-Langevin (ILL), Grenoble, France. The  STFC Rutherford Appleton Laboratory  is thanked fora ccess to neutron beam facilities. C omputing resources,( time on the SCARF compute cluster for the CASTEP calculations) was providedb yS TFC'se -Science facility. S ome of the computations regarding AIMD have been performed on the  UK National Supercomputing Facility ARCHER  (http:// www. archer. ac. uk) using our membership of the UK'sH  EC Materials Chemistry Consortium , which is funded by  EPSRC  grant  EP/L000202.
The chamber was mounted to I07's diffractometer, where data were collected using a 2D Pilatus photon detector. Measurements were carried out at room temperature with an incidence angle of 1°and a photon energy of 17. 7 keV (=0. 7 Å). The sample was mounted such that the surface was in the horizontal plane. A systematic series of X-ray reflections were acquired in a stationary geometry (lscans), that is, a full profile, including the background, is measured without scanning. For selected (h, k), the intensity profile at regular intervals of l-values is measured. The spot intensity distribution on the 2D detector of the measured reflection is then integrated and corrected,  20  enabling profiles of scattered intensity versus perpendicular momentum transfer for both crystal truncation rods (CTRs) and fractional order rods (FORs).
A reference reflection, (-1, 0, 0. 97), was recorded for both the clean and water covered surfaces at regular intervals to monitor surface degradation. No significant changes were observed throughout the duration of the experiment. A sizeable dataset was taken for the UHV prepared sample [TiO 2 (011)-(2 × 1)], comprising 10 419 reflections from 21 CTRs and 23 FORs that after averaging, using pg symmetry, were reduced to 5991 non-equivalent reflections. Subsequent to this, the chamber pump was switched off and the sample was exposed to a static pressure of ∼30 mbar of ultra-pure H 2 O via back filling of the chamber, which equates to about twelve monolayers. 21 The chamber pump remained switched off for the duration of measurements. A further 4978 reflections from 22 CTRs were measured from this surface that reduced to 3507 non-equivalent reflections after averaging by the same symmetry.
Water was thoroughly degassed using several freeze-pump-thaw cycles, with cleanliness confirmed by mass spectrometry (MKS Microvision Plus). Here, the partial pressure of water was increased to ∼10 mbar in the baby chamber and, through a precision leak valve, the differentially pumped mass spectrometer was exposed to a maximum of ∼1 × 10 -6 mbar H 2 O. Several masses were recorded simultaneously (O = 16, H 2 O = 18, CO = 28, O 2 = 32, CO 2 = 44, CH 2 O 2 = 29, 46 and CH 3 COOH = 43, 45, 60). While there was an increase of CO and CO 2 to ∼1 × 10 -8 mbar and ∼5 × 10 -9 mbar, respectively, there was no indication of formic or acetic acid evolution. The measured data were indexed with reference to a monoclinic unit cell defined by lattice vectors (a 1 , a 2 , a 3 ). Lattice vectors a 1 and a 2 are directed along the [1̅ 00] and [011̅ ] directions, respectively, and a 3 lies in the plane defined by the [011̅ ] and [011] directions at an angle of θ = 2 × [tan -1 (a/c)] with respect to a 2.
The numerical labeling of the atoms is employed in Tables  1  and S1  for the identification purposes. Symmetry paired atoms are denoted by an asterisk (*). For surface structure elucidation, the usual approach was adopted whereby theoretical SXRD data for a potential structure are generated. The structure is then iteratively refined to determine the best fit between experiment and theory. A modified version of the ROD software  23  was used for this task, in which the goodness-of-fit is measured by reduced χ 2. 24 ur DFT calculations were performed using the Quantum ESPRESSO package. 25 9] [30]  Wavefunctions were expanded in planewaves up to a kinetic energy of 25 Ry together with a cutoff of 200 Ry for the augmented density. We used ultrasoft  31  pseudopotentials with Ti(3s, 3p, 3d, 4s), O(2s, 2p), and H(1s) shells treated as valence electrons and modeled the unreconstructed rutile (011) surface using a 9.
We thank  Annabella Selloni  for useful discussions. This work was supported by the  European Research Council Advanced Grant ENERGYSURF  (G. T. ),  EPSRC (UK)  ( EP/D068673/ 1 ),  EU COST  Action  CM1104 , the  Royal Society (UK)  through a  Wolfson Research Merit Award  to G. T. , and  M. E. C. (Spain)  through project  MAT2015-686760-C2-2-P ,  Severo Ochoa   SEV-2015-0496  grant, and project  EFA194/16/TNSI  funded by interreg  POCTEFA program. M. H. M. A. acknowledges financial support from  EPSRC  through the  Advanced Metallic Systems Centre for Doctoral Training  ( EP/G036950/ 1 ) and from  BP. H. H. would like to acknowledge funding from  BP  through the  BP International Centre for Advanced Materials (BP-ICAM ). U. A. was supported by the  SNF Professorship  grant  PP00P2_157615. Calculations were performed on UBELIX (http://www.
All the tests were performed at 300 o C under an overall gas flow of 10 L h -1. This temperature is compatible with the ethylene epoxidation reaction. Two different reaction atmospheres were investigated: O2 in He (0. 8 to 8 % volume) and the ethylene epoxidation reaction mixture (4 % C2H4 / 1 % O2) at atmospheric pressure. The impedance data analysis was performed by the Z-view Software. For catalytic measurements, the sample was placed in a quartz reactor described elsewhere  [11]. The reactants and products were analyzed by an on-line micro gas chromatograph (μGC-R3000, SRA), composed of a BF PPU 8m/PPQ 1m column (CO2 analysis) and a VAR PLOT Q 8m column (Ethylene Oxide analysis). The detection limit for Ethylene oxide analysis was ~ 2 ppm. A light-off experiment was performed from 100 °C to 300 °C under ethylene epoxidation reaction conditions (4 % C2H4, 1 % O2, 3 L h -1 , He balance).
Raman spectroscopy was performed in-situ at 300 o C under an 8 % O2 (in N2, 1. 5 L h -1 ) reaction atmosphere in the region of 200-1600 cm -1 using a LabRam HR (Horiba) spectrometer equipped with a CCD detector cooled at -75 °C. Measurements were carried out under microscope with a laser wavelength of 514. 53 nm. A low dispersion grating of 1800 grooves/mm was chosen to get a maximum signal (to improve the resolution). The spectra were recorded and treated using the Labspec software (Horiba). Prior to the analysis, the instrument was calibrated using a silicon wafer.
Electrochemical properties in oxygen The oxygen electrode reaction on the Ag electrode was carried out under an oxygen-based reaction atmosphere (in the absence of ethylene). Figure  1  shows a typical impedance spectrum of the Ag/YSZ electrochemical catalyst under 8 % O2 at 300°C. The spectrum could be separated into two arcs located in the high frequency zone (1 MHz to 1 KHz) and two arcs in the low frequency zone (1 KHz to 1 mHz). To determine the contribution of each process of the oxygen electrode reaction, a typical equivalent circuit  [18]  composed of a resistance and a Constant Phase Element (CPE) in parallel for every depressed-arc was used to fit the data (see inset on  Fig 1). Usually, the YSZ solid electrolyte resistance produces at least two different arcs in the high frequency region, where the first one (highest frequency range) could be attributed to the charge transport through the bulk of the electrolyte, and the second arc is generally due to the transport through the grain boundaries  [4, 19].
Morphological biomarkers For each independent experiment, six watermilfoil shoots affixed onto Plexiglas squares were monitored in an aquarium for 19 days. Two squares per aquarium were laid out in the intermediate and laminar zones, as previously described in Section 2. 2. For each square, the mean total main shoot length (SL), mean total length of shoot ramifications (RamifL) and mean total root length (RL) were measured on plant pictures taken by a Sony camera (14 M pixels) every third day, then analyzed with the Visilog Viewer 6. 8 software (NOESIS).
Statistical analysis For each independent experiment and for each hydrodynamic zone, our results relied on three replicates (N=3), except for the growth parameters, which were based on six replicates (N=6). Data normality was verified by a kernel density estimation, along with typical Lilliefors and Shapiro tests. The effects of As on pigment content, hydrogen peroxide, osmotic potential and morphology at 21 days were assessed by the mixed ANOVA method, followed by a Sidak test to compare differences between control plants and arsenic-treated plants, as well as between younger and older plant parts. Arsenic accumulation in the various plant parts and the bioconcentration factor (BCF) were both tested by means of the Bonferonni test. All tests were performed at a 5% significance level using the Systat 11 software package for Windows.