dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding: This work was supported in part by an  International Research Partnership "Electrical Engineering-Thai French Research Center (EE-TFRC)"  under the project framework of the  Lorraine Université d'Excellence (LUE)  in cooperation between  Université de Lorraine and King Mongkut's University of Technology North Bangkok (KMUTNB) , in part by the  National Research Council of Thailand (NRCT)  under  Senior Research Scholar Program  under Grant No. N42A640328 , and in part by  King Mongkut's University of Technology North Bangkok  under grant  KMUTNB-66-KNOW-10.
Assessment of aggregate turnover We analysed the four different isotope labels in seven size fractions at three sampling times, i. e. , over two time intervals. We thus have more data points for each isotope label in different size fractions than in different time intervals, which precluded a quantitative assessment of gross microaggregate formation and turnover rates. Also, some macroaggregates were not fully dispersed when obtaining occluded microaggregate fractions. Here we thus focus on the relative distribution of the isotope labels across the different size classes over time to compare the effect of the different aggregate-forming materials on (micro) aggregate abundance and stability, and we used shifts in aggregate weight and isotope labels as indicators of net turnover rates.
Statistical analyses We used linear mixed-effect models with size fraction, sampling time, and treatment as fixed effects and pot ID as random effect. We also tested for interactions. This was performed in R (version 3. 6. 1, R Core Team, 2020) using the function lme available in the nlme package  (Pinheiro et al. , 2017). In case of significant effects, post-hoc tests were applied to investigate (i) differences between size fractions for each treatment and sampling time, (ii) differences between sampling times for each size fraction and treatment, and (iii) differences between treatments for each size fraction and sampling time. This was done using the function lsmeans of the lsmeans package  (Lenth, 2018)  with Tukey adjustment for multiple comparisons. Data were checked for normal distribution of residuals by visual inspection of histograms and QQplots.
Variance homogeneity was tested with Levene's test. As linear mixed effect models have been demonstrated to be robust against moderate violations of distributional assumptions  (Schielzeth et al. , 2020) , we decided to keep the model even in case of slight deviations. The qPCR data were analyzed by ANOVA with the factor strain included as additional factor, but without pot ID because of some missing data (cell numbers below detection limit). Further, the sum of strains was analyzed using a linear mixed effect model as described above, but with the following modifications: the effect of treatment was first tested excluding the 8000-2800 µm fraction as those data were largely missing in the bare treatment. Differences between fractions where then tested for the planted treatment and the bare treatment separately.
Acknowledgements We thank  Dr. Sneha Narvekar  for help in the laboratory, and the  German Science Foundation  for funding ( DFG FOR2179 ). The NaOH fusion digestion and ICP-OES measurements by  Nadine Wettengl , ZEA-3  Forschungszentrum Juelich , and the support of Fe and Si isotope ratio measurements by  Ulrike Seeling , ZEA-3  Forschungszentrum Juelich , are gratefully acknowledged.
Flow cytometry (FCM) measurements of morphological and physiological endpoints Samples at 24, 48 and 72 h were collected for each cellular parameter analysis, placed in a 96-well plate (300 µL per well, without cellular concentration standardisation), and analyses were performed using a MACSQuant X flow cytometer  (Miltenyi Biotec, Bergisch Gladbach, Germany). The 488 nm excitation laser was used for all the measured parameters, and B1-H (525-550 nm) and B3-H (655-730 nm) emission detectors were used for data collection. An acquisition threshold corresponding to the lowest possible size for the algal cells was set with FSC-H channel and used for all analyses, allowing us to remove unwanted particles (cellular or medium debris for example). The maximum number of measured events was set at 10 000 for each sample and the data used for most of the cellular parameters was the median intensity fluorescence (MFI) value, except for esterase activity and cellular viability expressed as a percentage of marked cells, and ROS formation with the use of the mean intensity fluorescence (Fig.
mL -1 (with a factor of two between each FDA concentration). Esterase activity was expressed as a percentage of cell considered metabolically inactive (located in a gated zone called FDA-on the left of the cytogram), active (located in a gated zone called FDA+ in the middle of the cytogram) and stimulated (located in a gated zone called FDA++ on the right of the cytogram) (Figure  F1-H ). For each of these gates, additional data were extracted on size and complexity, to dissociate mono-and multinucleated cells.
Statistical analyses Concentration-response curves used to determine the growth inhibition range of each experiment were modelled using R software (version 1. 4. 1717) with the rjags package and a R script version of MOSAIC growth  (Charles et al. , 2018). The script was run for each chemical and their 7 concentrations with the three-parameter log-logistic regression model (LL. 3) under a Bayesian framework with the following equation: Y = d / (1 + ( x e ) b ) (2) where Y is the growth rate (μ, h -1 ) corresponding to the concentration x and at 72 h, d is the upper-limit corresponding to the response value of controls, b is the slope of the curve and e is the concentration corresponding to the median effective concentration EC 50. The cytometry data for each sub-lethal parameter are presented in the Results and Supplementary material with standard deviation (SD) and statistical significance.
Effects of DBPs on ROS formation Data related to ROS formation are presented in Fig. 4. H 2 DCFDA allowed us to observe the intracellular changes in ROS formation of R. subcapitata. Each DBP induced significant ROS formation at some concentrations and times. Significant effects (p < 0. 05) were noticed at 24 h for MCAA for the highest concentrations tested and remained at 48 and 72 h. Significant increases in H 2 DCFDA fluorescence intensity were also measured for the other HAAs at the highest exposure concentration but only at specific time point and not continuously (48 and 72 h for DCAA, 48 h for TCAA, 24 and48h for BCAA) (p < 0. 05). However, the non-significant data still showed a systematic upward trend with the concentrations at every point in time. For 1,1-DCP, a significant increase was observed both at 48 h and 72 h for one intermediate concentration, 1.
Effects of DBPs on neutral lipid content Data related to lipid content are presented in Fig. 5. The cellular parameter neutral lipid content was shown to be sensitive to DBPs. At 24 h, a significant increase in fluorescence intensity was observed for all the DBPs concerning the mid and high range of concentrations tested and remained significant at 48 h and 72 h. However, for all HAAs, content of neutral lipids peaked on the first day of measurements (24 h) and declined significantly for most of the samples at 48 h (p < 0. 05) before stabilising at 72 h. Similar responses were observed for 1,1-DCP, except for the two highest concentrations where neutral lipid content remained stable at 24 h and 48 h and increased at 72 h (p < 0. 05). The temporal decreasing trend was recorded for controls on MCAA, TCAA, BCAA and 1,1-DCP experiments but remained less important than for exposed cells.
Computation and curve fitting We used Python version 3. 8. 2 and we implemented two programs based on the package scipy. optimize (curve_fit), one optimizing the fit with three free parameters (Q 2019 , τ, K) the other with two parameters (Q 2019 , τ) and the third set as follow: KðResourcesÞ ¼ Resourcesðt measure Þ þ X t measure t¼1900 PðtÞ ð9Þ KðReservesÞ ¼ Reservesðt measure Þ þ X t measure t¼1900 PðtÞ ð10Þ With t measure the date corresponding to the latest data available. Remark: When t measure = 2021, P(t measure ) and P(t measure -1) do not exist yet, because USGS report delivers annual production with a delay of two years. It results on an underestimation of K as presented in S1 Fig of the supporting information. It presents an illustration of this mathematical artifact. Thus, a correlation coefficient 0 < R 2 < 1, using the method of least squares, is computed after each method implementation and for defining equivalent zones.
It is calculated using the following equation: R 2 ¼ 1 N � X N t¼1 ðX i À Y i Þ 2 ð11Þ With X and Y being respectively the production data and simulated production vectors and N equals to the length of the production data vector. A R 2 equal to 1 means the model is perfectly correlated to data. Figures have been created with the package matplotlib. pyplot (plot, contour and coutourf). The results of these curve fitting in presented in the supporting information, in temporal representations (see S2, S3 and S4 Figs from the supporting information.
Anomalous Small Angle X-Ray Scattering (ASAXS) Data acquisition and processing were performed according to the method adapted by Humbert et al. [12]  on CoMoS catalysts. Sulfide extrudates were crushed and put into 1. 5 mm diameter quartz capillaries in a glove box. Anomalous small-angle X-ray scattering experiments were performed on the D2AM beamline of the ESRF synchrotron. The scattered intensity was measured slightly below the molybdenum K-edge of absorption at the five following energies: E 1 (19700 eV), E 2 (19860 eV), E 3 (19940 eV), E 4 (19975 eV) and E 5 (19990 eV). Calibration of the incident beam energy was carried out with a metallic molybdenum foil. Two sample-detector distances (35 mm and 3600 mm) were chosen to cover the largest q range. The scattering images were recorded using a XPAD detector.
The acquisition time (180 s) was determined in order not to saturate the detector. Moreover, a homemade beamstop, consisting of 2 copper square-shaped sheets of 50 μm thick, was placed on the detector to decrease the intensity scattered and recorded at small angles. For 1D reduction, raw data were corrected by a "flat" to remove the beamstop influence, and normalized by the incoming photon flux, transmission and true thickness calculated from the transmission coefficient. A glassy carbon was measured at each energy and for both sample-to-detector distances to calibrate the intensity in absolute units, i. e. , as the differential scattering cross section per unit volume in cm-1. A correction factor was applied from the ratio of the NIST data to the experimental data. The theoretical aspects of ASAXS technique are exhaustively written in S.
Acknowledgements ASAXS experiments were performed on BM02-CRG beamline at the  ESRF. We are grateful to the beamline staff for smoothly running the facility. We would like to also thank  Estelle Péan ,  Hedwige Poncet ,  Saloua Sahal-El Ahrache  and  Dr. Christèle Legens  for their contribution in catalysts preparation, EPMA and XPS analysis, respectively.
Interestingly, the authors identify phasing-in effects as carbon leakage increases over time, which they interpret as resulting from costly adjustment of production capacity. Colmer et al. (2022)  use French firm-level data, which allow them to compare, using a difference-in-difference design, regulated and unregulated firms within each sector. They find no evidence that the EU-ETS shifted production and emissions to unregulated firms or out of the regulated zone through trade. Dechezleprêtre et al. (2022)  investigate the relocation channel. They look at the behavior of multinational companies, which are the most likely to relocate to avoid climate policy. Using the Carbon Disclosure Project (CDP) database (composed of 1,122 companies, of which 216 are subject to the EU-ETS), they find no significant effect of the EU-ETS in the period 2007-2014.
Effects of variations in energy prices. A study by  Misch & Wingender (2021)  is an exception in the literature. These authors use sector-country-specific data on policy-induced changes in energy prices and country-sector data on carbon embodied in trade flows (the Trade in Embodied CO 2 database of the OECD). They estimate elasticities of the carbon content of production, imports, and exports and retrieve leakage rates using Equation  1. They find very significant leakage rates, with an average of 25% over their sample of countries and an average of around 15% for 14 European countries plus the United Kingdom. Their results tend to confirm that the weakness of the leakage obtained in previous studies is at least partially due to the weakness of the carbon price in the EU-ETS.
Effects of environmental regulations. In a series of papers,  Aichele & Felbermayr (2012 , 2013 , 2015)  look at the effect of the Kyoto Protocol on carbon emissions. They find much more evidence of adverse effects of environmental policy on competitiveness, though they did not clearly investigate the channel through which these effects materialize. Aichele & Felbermayr (2012) evaluate the effects of commitments made by some countries under the Kyoto Protocol. These commitments are supposed to take effect on the date of ratification of the Kyoto Protocol by the national parliament. The authors build a panel database on carbon embodied in trade and carbon footprints, using MRIO tables. Their sample of countries covers more than 80% of world carbon emissions over the period 1995-2007. Using a first-difference instrumental variables estimation strategy, they find that domestic emissions have decreased by 7% in committed countries but that carbon footprints have increased, providing evidence of carbon leakage.
Such a conception of the exercise may seem peculiar in the current debate, but recall that it was envisaged by the European Commission during the preparation of the Rio Summit (and is also known as the Ripa di Meana proposal, named after the Commissioner for the Environment). Böhringer et al. (2018)  use a general equilibrium model featuring a more detailed database and obtain, depending on the parameterization, a 9-23% leakage rate induced by a 20% reduction in carbon emissions relative to 2011 emission levels across the OECD. Another series of ex ante general equilibrium studies assesses the leakage rate associated with the Kyoto Protocol, obtaining leakage rates of at most 25%  (Elliott et al. 2013 , Paltsev 2001 , Weyant 1999). A comparative modeling exercise surveyed by  Böhringer et al.
A5. Global Trade Analysis Project GTAP is an international network of researchers and policy makers who conduct quantitative analyses of international policy issues. At the heart of the project is a global database describing bilateral trade patterns, production, consumption, and intermediate use of goods and services, with an environmental dimension. The tenth version of the database  (Aguiar et al. 2019 (Aguiar et al. ) covers the years 2004 (Aguiar et al. , 2007 (Aguiar et al. , 2011 (Aguiar et al. , and 2014  and breaks down the world economy into 65 sectors and 141 countries/regions, accounting for 98% of world GDP and 92% of world population. The GTAP-E satellite database provides CO 2 emission data disaggregated by fuel and user for each of the 10 GTAP countries/regions. The data are based on the extended energy balances compiled by the International Energy Agency.
A8. European Union Transaction Log The EUTL is the transaction log required by Article 20(1) of the EU-ETS Directive for the purpose of registration, issuance, transfer, and cancellation of allowances (Eur. Council 2023). Through the EUTL, the European Commission provides public information on the compliance of regulated entities, active participants in the system, and transactions between these participants. Transactions can be recovered at https://ec. europa. eu/clima/ets/transaction. do. However, accessing the data is not convenient, and a relational database connecting the different elements of the EUTL has been made public  (Abrell 2022 ). The corresponding data set is available at https://euets. info/static/download/Description_EUTL_database. pdf.
CO 2 emissions (log) 40 Water transport Trade openess (%) 10 20 30 Financial intermediation Equipment and machine rentals trade Wholesale mineral nuclear fuel Air transport Other nonmetallic 0 Real estate Retail fuel vehicle repair Electricity/gas/ water supply 4 5 6 7 8 Total Figure 2 Total CO 2 emission intensity and trade openness (defined as international trade divided by gross output) from the five most-emitting and five least-emitting industries. Emission rates are (log) metric tons of CO 2 per million dollars of output in 2009, after inverting the World Input-Output Database multiregional input-output table. Figure adapted from Copeland et al. (2022). using the World Input- Output Database (WIOD) MRIO table. With the exception of transport, services are among the least-emitting sectors.
For each sample, 100 spectra with a resolution of 4 cm -1 had been collected in the wavenumber range from 100 to 6000 cm -1. 57 Fe Mössbauer spectra were collected using two conventional spectrometers in transmission geometry coupled with a cold head cryostat from Advances Research Systems (USA) or JANIS Research (USA), equipped with vibration isolation stand, developed in LCPME laboratory. The 50 mCi 57 Co in Rh matrix radioactive source was mounted in a constant acceleration velocity transducer. Measurements were taken at the wide range of velocity ±11 mm s -1. The hyperfine interaction parameters were determined by fitting the experimental spectra by a least-squares method using the Recoil software  [37]. The center shifts were reported with respect to that of 25 μm-thick α-Fe foil at room temperature.
Acknowledgements The authors would like to thank  Sylvie Migot  and  Jaafar Ghanbaja  from  CC3M  ( IJL-Nancy ) and  Liva Dzene  ( IS2M, Mulhouse ) for TEM imaging. We thank the PMD2X X-ray diffraction facility and  Pierrick Durand  ( CRM2-Nancy ), for X-ray diffraction measurements, data processing and analysis, and providing of reports for publication: http://cr m2. univ-lorraine. fr/lab/fr/services/pmd2x. FTIR, Raman and Mössbauer spectrometric measurements were performed at the spectroscopy and microscopy service facility of  SMI LCPME (Université de Lorraine-CNRS  https://www. lcpme. ul. cnrs. fr/equipements/smi/). The authors would like to thank  Aurélien Renard  ( SMI LCPME, Nancy ) for help with SEM measurements. The authors would like to thank  Stephane Suire  and  CC MagCryo  ( IJL-Nancy ) for help with magnetometry measurements.
2 conversion Plasma-catalysis Carbon capture and utilisation (CCU) Energy efficiency Specific energy input Dielectric barrier discharge Packed bed reactor Methanation Dry reforming of methane a b s t r a c t This paper brings the comparison of performances of CO 2 conversion by plasma and plasma-assisted catalysis based on the data collected from literature in this field, organised in an open access online database. This tool is open to all users to carry out their own analyses, but also to contributors who wish to add their data to the database in order to improve the relevance of the comparisons made, and ultimately to improve the efficiency of CO 2 conversion by plasma-catalysis. The creation of this database and database user interface is motivated by the fact that plasma-catalysis is a fast-growing field for all CO 2 conversion processes, be it methanation, dry reforming of methane, methanolisation, or others.
As a result of this rapid increase, there is a need for a set of standard procedures to rigorously compare performances of different systems. However, this is currently not possible because the fundamental mechanisms of plasma-catalysis are still too poorly understood to define these standard procedures. Fortunately however, the accumulated data within the CO 2 plasma-catalysis community has become large enough to warrant so-called ''big data" studies more familiar in the fields of medicine and the social sciences. To enable comparisons between multiple data sets and make future research more effective, this work proposes the first database on CO 2 conversion performances by plasma-catalysis open to the whole community. This database has been initiated in the framework of a H2020 European project and is called the ''PIONEER DataBase".
Thus, this work proposes a meta-analysis tool, suitable for CO 2 plasma-catalysis, with the aim of facilitating the identification of key parameters and trends for performance improvement. The proposed tool is an open access database collecting all the relevant performance data from the literature. The database is the result of a joint effort between 13 PhD students (first authors of this work), part of the European PIONEER project  [26] , and is therefore called the PIONEER database (abbreviated as CO2-PDB from now on). In addition, an online user interface, allowing the extraction, filtering and plotting of the data from the database has been developed and is accessible via http:// db. co2pioneer. eu (and aliases http://pdb. co2pioneer. eu or http://database. co2pioneer. eu or http://pioneer.
database. co2pioneer. eu). More details, e. g. , practical information to get started, using and referencing the CO2-PDB, can be found in the online documentation: https://docs. co2pioneer. eu. The CO2-PDB is exclusively limited to plasma-(catalysis) CO 2 conversion results. This is motivated by the specificity of plasma-catalysis for CO 2 conversion, as will be discussed in Section 2. CO 2 capture and storage technolo-gies can also sometimes rely on the use of catalysts (or adsorbents), which are sometimes even produced using plasma processes  [27] , but these technologies, although certainly useful in the context of reducing CO 2 emissions, are not included in the database proposed here. The positioning compared to other existing databases, the graphical interface and the type of data set to be provided to the CO2-PDB are described in Section 3.
3. The explanation of how to use the basic and more advanced features of the CO2-PDB will be explained in Sections 3. 3. 1 and 3. 3. 2, respectively. Section 4 provides first examples of data extraction from the CO2-PDB. A comparison with figures from previously published review papers in the field of plasma-catalysis is first shown in Section 4. 1. Section 4. 2 explains in a broad manner the use of the CO2-PDB web interface. The caveats and disclaimers are explained in details in Section 4. 3. To illustrate the possibilities offered by the numerous input parameters of the database, Sections 4. 4 and 4. 5 highlight the role of the support vs. the role of the active phase for CO 2 conversion performances and also the behaviour of C x H y molecules in various plasma-catalytic reactor configurations.
The graphs that will be discussed in these data extraction examples are obtained from the data set already included in the CO2-PDB at the time of submission of this paper. It is important to reiterate that this data set is intended to be developed by contributions from the whole community. The trends observed will therefore need to be confirmed against a larger and perhaps better defined data set when future publications in the field provide more complete information on the experimental conditions and characterisation of the materials used. Currently many publications suffer from a lack of characterisation of either the plasma source or the catalytic material used. This is an important obstacle to more detailed comparisons of the results obtained in different research groups. Section 5 will therefore propose some ways to enrich future work with minimal parameter sets (which can be included in the CO2-PDB) that seem important to gain more understanding from cross comparison of results of different works.