dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and variables Our daily dataset covers the period from 1 January 2020 until 1 January 2021, fully capturing the recent Covid-19 pandemic. The prices of the S&P 500 stock index, the 10-year US benchmark government bond index, and the carbon dioxide emissions allowances (EUAs) are retrieved from Refinitiv Eikon Datastream. All price data have been transformed into daily returns, using the formula (see e. g. Michaelides, Tsionas and Konstantakis, 2016) : Returns p t ln P t P t-1 , t 1,. T (26) The data on the Covid-19 new cases are also in daily frequency and come from the Johns Hopkins University database, which is freely accessible to the public. Table  1  below provides a compact description of the data.
Date stamping of the two covid-19 waves Throughout the entire ongoing period of the pandemic, the preferences of investors and firms have changed based on each wave of the pandemic, since different economic and lockdown measures have been implemented in each wave. Therefore, in order to capture these shifts, and extract vital information rgerading the expected future quantity of carbon emmisions, we need to extend our analysis by capturing the two waves in the Covid-19 era that span our dataset. In order to accurately time stamp the two waves, we make use of the popular state-of-the-art sup-ADF test by  Phillips and Shi (2020)  and  Phillips et al. , (2011 Phillips et al. , ( , 2015)). Figure  1  presents our findings. Note that since we are interested in dating the two wave periods of Covid-19 and not the explosive behaviour of Covid-19, based on the sup-ADF test, we will also include in each wave the beginning and the end of the explosive nature of the Covid-19 pandemic.
ACKNOWLEDGMENTS G. P. acknowledges support from the  DFG (German Research Foundation)  under Germany's  Excellence Strategy, cluster   EXC 2037  " Climate, Climatic Change, and Society " (project  390683824 ). M. P. , G. P. , and M. W. acknowledge support from the  German Federal Ministry of Education and Research , project  ARIADNE  ( BMBF  project  03SFK5S0 ).
The software of Nano Measurer 1. 2 was used to calculate particle sizes. Elemental analysis of as-prepared catalysts was carried out by an inductively coupled plasma atomic emission spectroscopy (ICP-AES). H2 chemisorption at 30 °C was used to measure the Ni dispersion using ASAP 2010C apparatus from Micrometrics. Catalyst weight ca. 200 mg was loaded into a Pyrex tube, and subsequently degassed in He for 30 min. After evacuation, the sample was reduced in pure H2 at 500 °C for 2 h (same as in situ reduction for methanation, see Section 2. 3) followed by purging with He for 1 h and adsorption of H2. Two isotherms were measured in the range of 0. 13-60 kPa. The first isotherm accounts for reversible physisorption and irreversible chemisorption. The sample was evacuated at 30 °C to desorb reversibly adsorbed H2.
No quantitative links with the electrochemical processes and their temporal evolution are displayed. Yong-liang Wang et al. proposed a numerical approach to investigate the role of gaz bubble on cell voltage oscillations. They treat an whole aluminium electrolysis cell as a resistance circuit  [18]  and model the dynamic simulation of the cell equivalent circuit with Matlab/simulink software. This work shows that bubble departure fre-quency has an important effect on voltage oscillations. When the bubble coverage of one anode block exceeds 80 % of the surface, the cell voltage may exceed its normal fluctuation amplitude. Although interesting and meaningful, this work artificially introduces the departure frequency of the bubbles and does not relate it to either the electrochemical reaction or to the physics of the bubble detachment process.
Here, we propose numerical computations for bubble detachment on a gas evolving electrode. These computations predict the potential evolution of the electrode for a given current density while taking into account the oscillations due to local detachments. The originality of our approach lies in the description of the electrode which feature (number and heterogeneities of bubbles nucleation sites, Fritz radius associated with the site) vary as a function of the working conditions. In contrast to the previously mentioned works, we link the bubble detachment and the electrochemical reaction. This approach allows us to predict the stability of the system and the maximum current density that can be used before reaching an unstable potential. A comparison with experimental data is given. The experiments are performed on a Platinum/Carbon cell in presence or not of electrolyte flow.
Data acquisition Chronopotentiometry experiments are performed to investigate and reproduce by simulation the effect of bubbles detachment at the platinum gas evolving electrode. All the experiments are performed using a potentiostat (Bio-Logic, VSP200). A constant current is applied, the electric potential of the platiumum electrode versus a Ag/AgCl reference electrode is recorded over time. Before applying the current density, a strong flow (Q= 10ml/min) is applied in the cell for 5 minutes in order to sweep the residual bubbles. The flow is then stopped after one minute the electrical current is applied.
The dynamic PSCs are simplified to three types based on the varying orientation: horizontal, vertical, and diagonal. Simulations are carried out with Matlab & Simulink. The performance comparison for the four traditional PV array and two reconfiguration techniques is based on daily energy losses. The results show that four traditional PV configurations techniques, in all PSCs' scenarios, EAR has the most stable performance and the lowest energy losses. The energy losses of SP connection are the largest in all PSCs cases. Although their performance varies depending on the partial shading case, Total-Cross-Tied and SudoDKu lead to the lowest energy losses.
In the D-PSCs cases, the shape of the shadow can shrink or extend in one direction. In this paper, assumptions of the D-PSCs are made according to the direction of the varying shadow. Three directions (Horizontal, Vertical, and Diagonal) of the PSCs' varying will be considered. Their effects on various reconfigured layouts (the four typical PV configurations, PAR and EAR) will be evaluated. The irradiance data are obtained from the PVGIS database. The output performance of different PV reconfiguration techniques will be compared based on daily energy losses analysis.
In the series-connected PV modules, the diodes are called bypass diodes, while in the parallel-connected PV modules, they are blocking diodes (anti-reverse diodes). To better understand the PSCs on different PV configurations, this section illustrates the operating mechanism for the two basic PV configurations. The analysis starts with a uniform irradiance condition before tackling the PSCs. The basic PV configurations refer to the PV layouts in Figures  1a  and 1b. The analysis is based on the PV panel model in Simulink. Figure  1c  is the classical equivalent electrical circuit single-diode model. Figures  1d, 1e , 1f represent a single PV model, a series-connected PV configuration model, and a parallel-connected PV configuration built in Simulink, in which the DC source acts as a load by adjusting the output voltage.
Irradiance data The irradiance data is from the open-access database of PVGIS  [21]. PVGIS is an online PV parameter tool offered  by Europe Commission. In this article, the selected location for the PV system is in Palaiseau, France (48. 72°N, 2. 25°E). The curves of the best (August) and worst (January) daily global irradiance (Tilted irradiance, the slope of the PV panel is 35°, the azimuth angle of the panel is 0°) are displayed in Figure  7a. In this work, the best daily irradiance is considered. Figure  7b  gives the composition of the daily irradiance. The global irradiance is the sum of the direct and diffuse irradiances. In this paper, the global irradiance is applied on the PV array as the uniform irradiation condition. When PS occurs, only the diffuse irradiance is considered.
Structural considerations We examined 274 group B sequences listed on the hydrogenase database website 21 as of may 2023  22  (we removed WP_025640716. 1, which lacks the P1 motif, and included the CpIII and MeII sequences). In this group, the xTSCCPxx motif is the most common version of P1 (134 occurrences), followed by TSCCCPxx (75 occurrences). The latter is specific to group B: none of the 955 hydrogenases sequences classified as group A or C listed on the hydrogenase database included a CCC triad. In all sequences in group B, the first run of four cysteines, which is Cx2Cx2Cx3C in prototypical hydrogenases, is highly variable (blue dots in SI fig  S6 ), with a consequence on the coordination of the accessory [4Fe4S] clusters that is distal from the active site (SI figure  S7 ), as mentioned before  10, 18.
If we note k i the pseudo 1st-order rate constant of production of I 2 (inactivation) and k a the rate of disappearance of I 2 (activation), the hypotheses translate into: (4) (5) (6) • The rate constants k i and k a depend on potential in a sigmoidal manner: Note that k i tends to k i max at high potential, and k a tends to k a max at low potential. This (in)activation kinetics is expected if the formation of I 2 follows an "ECE" mechanism (using the electrochemical terminology), with an oxidation (e. g. the formation of Hox, the most oxidized catalytic intermediate) that precedes the inactivation, a chemical transformation, and a final oxidation that locks the inactive state. But we make no a priori assumption on the values of E 0 2 and E 0 3. We used the QSoas software  29  to fit the model to two successive scans to accurately determine the kinetics of slow reactivation at low potential.
Acknowledgements This research was funded by the  Centre National de la Recherche Scientifique ,  Aix Marseille Université ,  Agence Nationale de la Recherche  ( ANR-21-21-CE50-0041 ),  Région Sud. This work received support from the french government under the  France 2030 investment plan , as part of the  Initiative d'Excellence d'Aix -Marseille Université -A * MIDEX ,  AMX-22-RE-AB-097. The authors are very grateful to  Frédérique Berger , glass blower at  Aix Marseille University  and the proteomic facility of the  Institut de Microbiologie de la Méditerranée (IMM, CNRS-AMU , FR3479),  Marseille Proteomique (MaP) , for performing proteomic analyses by mass spectrometry.
Acknowledgements We express our gratitude to  Dr Jean-Blaise Brubach  of the AILES beamline of  Synchrotron SOLEIL  for his help during infrared spectra collection. We are indebted to  Ms. Chanjuan Zhang  and  Dr Zakaria Halime  at the  Institut de Chimie Moléculaire et des Matériaux d'Orsay, Université Paris-Saclay  for their assistance in performing 1 H-NMR measurements. The  Agence Nationale de la recherche  is acknowledged for a young researcher grant to B. L. -K. (grant no. 18-CE05-0007 ).
Acknowledgements This work received funding from the  Swiss National Foundation  (Grant  159716 ,  FN 7359 ),  French National Research Agency (Labex ARCANE ,  CBH-EUR-GS ,  ANR-17-EURE-0003 ). MN acknowledges the  University of Ferrara  ( FIR2021 ,  FAR2022 ) for funding. Computational resources from the mésocentre  EXPLOR of Université de Lorraine  (Project  2018CPMXX0602 ) are acknowledged.
The daily activities of ≈8 billion people occupy exactly 24 h per day, placing a strict physical limit on what changes can be achieved in the world. These activities form the basis of human behavior, and because of the global integration of societies and economies, many of these activities interact across national borders. Yet, there is no comprehensive overview of how the finite resource of time is allocated at the global scale. Here, we estimate how all humans spend their time using a generalized, physical outcome-based categorization that facilitates the integration of data from hundreds of diverse datasets. Our compilation shows that most waking hours are spent on activities intended to achieve direct outcomes for human minds and bodies (9. 4 h/d), while 3. 4 h/d are spent modifying our inhabited environments and the world beyond.
Because global outcomes emerge from the sum of individual actions, it is crucial to understand how global activities influence local changes and vice versa. Compounding the disciplinary divisions is a geographic fragmentation of activity data. The collection and analysis of activity data have tended to be carried out at the national scale, tailored to the specific needs and objectives of individual countries, and most analyses have focused on wealthy populations. Although there have been some efforts to compare sociological time use data for adults across countries  (16) (17) (18) (19) (20) , they have not previously been used to characterize the global human system in a broader sense. Economic activities are more often captured in global databases (  21 ), yet where global economic analyses have been carried out, they have typically focused on individual sectors of the economy, or relied heavily on monetary valuations to combine activities among countries.
A Holistic Estimate of Global Time Use Here, we assemble a complete estimate of what humans are doing, averaged over time and across the entire population, to provide an aggregated high-level view that we refer to as the global human day. We combined available data collected by national statistics agencies, international organizations, and researchers from over 140 countries, wherever available during the period 2000 to 2019 to avoid the economic and social disruption of the COVID-19 pandemic (SI Appendix, Tables  S1  and S2 ). We interpolated within geographical regions to countries with incomplete or missing data in order to account for undersampled populations. We assessed the full human lifespan by weighting population-specific time use estimates using agestructured demographic data (Methods).
Our approach is enabled by a generalized categorization of activities  (29) , the Motivating-Outcome-Oriented Generalized Activity Lexicon (MOOGAL), which allows for the integration of data originally collected for diverse sociological, economic, and anthropological purposes. The lexicon is comprised of eight categories (Table  1 ), which are subdivided into 24 subcategories (Table  2 ). The subcategories are described in physical, rather than colloquial, terms to limit ambiguity in their application across cultures. Since the MOOGAL lexicon is designed to combine economic and noneconomic data, it differentiates based on the motivating outcome that causes the activity to be undertaken, rather than whether or not the activity is undertaken for pay. For example, both paid daycare work and unpaid care of young children by parents are classified under physical childcare, while food preparation includes both cooking at home and working at a restaurant.
Methods Our estimate of the global human day is constructed as part of the Human Chronome Project (https://humanchronome. org/), based on three primary components, namely nationally representative time use surveys, national statistics of employment and working time according to economic activity, and a multicomponent time use model for youth aged 0 to 17 y. We also include sleep data from wearable devices for comparison with time use survey data. The data sources are briefly described below, followed by the strategy used to interrelate activities through a common lexicon, and a brief discussion of uncertainty. A more thorough description of the method is provided in SI Appendix, including supplementary figures and tables. Time Use Surveys. To provide a baseline for average daily time use, we obtained nationally representative time use surveys from 58 countries comprising approximately 60% of the world population.
These surveys are conducted by national statistical agencies with the goal of providing a broad understanding of how the population allocates their time to a set of activities. Most of these surveys report any formal economic participation as a single activity, e. g. , "work for employment" (with some exceptions). Survey data are collected via self-completed time diaries, telephone or in-person interviews, online questionnaires, or a combination of these methods. The measure we use is the population-weighted average daily time spent on each activity among all respondents, which is the product of the participant time (the average time spent on each activity only among those who engaged in the activity) and the participation rate (the percentage of all respondents engaging in the activity). When available, the aggregate survey data were downloaded from the respective national statistical agency database, and translated as necessary.
If the aggregated datafile could not be located, the relevant table from the survey report was manually transcribed to a computer-readable format. The full list of countries, including the source location, is given in SI Appendix, Table  S1. The quality of each survey is assessed according to several key characteristics, notably survey duration, data collection method, and lexicon length. Three quality levels (A, B, and C) are associated with a 5, 10, and 20% baseline uncertainty on the time values reported in the survey, respectively. For details regarding the quality assessment, see SI Appendix, section 6. Economic Activity. The main source for economic data was ILOSTAT, the online repository of labor statistics managed by the International Labour Organization (ILO) Department of Statistics.
Mean annual employment and mean weekly working time data, recorded under the International Standard Industrial Classification (ISIC) of economic activities, were used to calculate mean daily working time of the entire population. We also obtained the comparable economic activity data for Canada, China, Japan, and Russia, which did not archive data with the ILO. In total, economic data were available for 139 countries, representing 86% of the world population. Youth Population. While the time use surveys are nationally representative, many do not include youth below a minimum age, which varies by country between 14 and 18 y. The absence of children from most time use surveys can be generally attributed to the view of time use as a metric of human capital, a context in which children are not considered useful  (44).
In order to correct for the consequent bias, while also providing a complete description of human time, we assembled a complementary dataset on the time use of youth aged 0 to 17 y and used this to construct an age-structured model of total youth time. Student enrollment data from the World Bank and UNICEF were paired with educational instruction time from the OECD to calculate average daily time spent in schooling for each age. Youth employment data were taken from the 2016 ILO Global Estimates of Child Labour report, which covers children between ages 5 and 17 y in 105 countries and provides child employment rates in agriculture, industry, and services. Youth working hours were obtained from the World Bank. The activities occupying the remainder of average daily time were estimated using data averaged from time use studies conducted in 10 countries, as well as a global youth sleep time study (see SI Appendix, section 4 for details).
Given the diversity of our data sources, it was necessary to crossmap across a large number of activity categorization systems, known as lexicons. Activities were reclassified according to the motivating outcomes that cause the activities to be undertaken, using the MOOGAL as described in ref. 29. The MOOGAL subcategories are intended to apply to any human population and epoch and are generally well aligned with commonly used sociological and economic lexicons (HETUS, ICATUS, ISIC) for the majority of activities. Activities that are coordinated between multiple people, such as those carried out in economic activities organized PNAS 2023 Vol. 120 No. 25 e2219564120 https://doi. org/10. 1073/pnas. 2219564120 9 of 9 by firms, are categorized according to the motivating final output, consistent with standard economic practice.
A weighting of 1 indicates that the activity is uniquely associated with the single subcategory, while a weighting of 0 indicates that the activity is entirely excluded from that subcategory. An activity that cannot be entirely associated with a single subcategory is split fractionally among subcategories, that together sum to 1. All activity definitions were estimated independently by at least three coders, discrepancies were reviewed, and mean values were used wherever unequal but defensible estimates co-occurred. Interpolation to Countries with Missing Data. Our dataset includes direct observations for 145 countries. Of these, both time use and economic data were available for 52 countries, time use data alone were available for 6 countries, and economic data alone were available for the remaining 87 countries.
To assess the entire global human population, we group countries into 17 geographic regions based on the ILO subregions and separately interpolate both time use and economic data to the missing countries in each region. For each missing data type, countries are filled using the population-weighted average of the sampled countries in the same region, for each subcategory. Data, Materials, and Software Availability. All data used in this work, as well as scripts to compile the results, are deposited in zenodo (10. 5281/ zenodo. 7941615)  (47).
ACKNOWLEDGMENTS. We thank  Victoria Reyes-García ,  Jeroen Van Den Bergh ,  David Carozza ,  Dror Etzion ,  Majdi Hunter-Batal ,  Maxwell Kaye ,  Maria Pastor ,  Lior Greenspoon ,  Eric Kolaczyk  and  Brian Robinson  for helpful comments and discussion. Author affiliations: a  Department of Earth and Planetary Sciences, McGill University, Montréal, QC H3A 0E8, Canada ; b  Institut de Ciència i Tecnologia Ambientals, Autonomous University of Barcelona ,  08193 Cerdanyola del Vallès, Barcelona, Spain ; c  Institute for Health and Social Policy, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC H3A 1G1, Canada ;  d Bieler School of Environment , McGill University, Montréal, QC H3A 2A7, Canada ;  e Centre for Population and Development, Institute of Research for Development, University of Paris ,  75006 Paris, France ; f  Laboratoire de Météorologie Dynamique ,  Institut Pierre Simon Laplace ,  Ecole Normale Supérieure Ulm, 75006 Paris, France ;  g Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel ; h  Data Science Institute, Columbia University, New York , NY 10027; i  Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal ,  QC H3A 1Y7, Canada ; and  j Institute of Public Health, Epidemiology, and Development ,  College of Health Sciences, Université de Bordeaux, Bordeaux  33076,  France.
210 • C after calibration process. National instruments data acquisition system NI cDAQ-9174, a compact DAQ system, is used to record all temperatures, pressures and flow rates throughout each experimental test as shown in Fig. 2(b). Then the data are read, processed, and stored using a program running in LabView with a frequency of one measure per second (National Instruments, Online). Besides, the solenoid valve is controlled remotely by a Carel controller. Before starting the tests, the experimental loop is fully filled with water and then air is vented from high points, making sure that the whole circuit is free of air. The dry cooler circuit flow rate is manually adjusted depending on required heat load and temperature difference of 20 K. Cooling pads flow rate is adjusted depending on manufacturer recommendation and external conditions.