dataesr/scientific-paragraphs-categorization huggingface dataset:
Electricity information is obtained from two different Spanish national sources. On the one hand, IC data until the end of 2020 at the municipal level is available from the Spanish Ministry for the Ecological Transition and the Demographic Challenge from the time when renewable energy installations were first registered. These municipal ICs are assigned to the nearest model grid point so that each point aggregates IC from the municipalities around it. Then, regional monthly energy generation and demand information, as well as yearly regional IC data, were obtained from the Red Eléctrica de España (REE). Since CFs are calculated from a combination of Ministry and REE data, comparisons between monthly regional REE ICs and Ministry ICs are used to validate the outcomes. The resulting IC is therefore an accurate representation of the actual distribution of PV and wind installations over the model domain, either in the regional or the grid point-based approach (Fig.
Acknowledgements This research is framed within the TRAMPAS ( PID2020-113036RB-I00/AEI/10. 13039/501100011033 ) project. Author A. Maimó-Far received funding from the  Spanish Ministerio de Ciencia Innovación y Universidades  ( FPU18/00520 ). This research made use of e4clim, an open source software, developed in the frame of the  Energy4Climate Interdisciplinary Center (E4C) of Institut Polytechnique de Paris and Ecole des Ponts ParisTech  and supported by the  3rd Programme d'Investissements d'Avenir  [ ANR-18-EUR-0006-02 ] and by the  Foundation of Ecole Polytechnique (Chaire Défis Technologiques pour une Énergie Responsable).
The framework is only intended for medicines already out of intellectual property, data or marketing protection. The "champion" would typically be a "not-for-profit" organisation, for example an entity or a person from a charity or patient group, academic unit, learned society, research funder or payer. The framework builds on existing regulatory tools, namely support from the EU-Innovation Network (EU-IN) and scientific and/or regulatory advice provided through EMA and/or a NCA to provide guidance to the champion. The framework foresees that the champion would in time liaise with the marketing authorisation holder (MAH) of the potentially repurposed medicine, with the expectation that the latter can initiate an application for the new indication of use (i. e. , on-label use) of the medicine by applying for variation, line extension or new marketing authorisation to a regulatory authority.
EMA and the NCAs have been approached by sponsors in health care for central and national scientific advice on clinical trials in possible repurposing projects. In August 2020 there were more than 300 substances and 1,000 clinical trials recorded in a database produced by the Anticancer Fund 4  (20). Moreover, in February 2021 there were around 1,600 drugs by over 1,000 sponsors in clinical trials recorded in a database by Informa Pharma Intelligence  5  It is clear that repurposing of well-known medicines for COVID-19 disease is done extensively. Outside the scope of COVID-19, repurposing also seems to have taken off  (21) (22) (23). First, it has shown the various angles of repurposing, from medicines that have never been approved before but have been the subject of clinical development in other indications (like remdesivir), medicines approved but still within protection periods (e.
TNF-alpha inhibitors have revolutionized the therapeutic care in chronic inflammatory diseases. Several biosimilar products were commercialized at their patent expiry, substantially decreasing the cost of treatment. This longitudinal descriptive study aimed at assessing infliximab, etanercept and adalimumab biosimilar penetration rates using data of the French National Health Data System. A total of 207,118 new or prevalent users from the date of first biosimilar commercialization in France (respectively January 2015, May 2016 and October 2018) were included in the study and followed until September 30, 2021. Biosimilars represented respectively 78%, 46% and 53% of the overall initiations, and 94%, 66% and 60% last year's initiations. A total of 46%, 19% and 17% of originator product prevalent users switched for a biosimilar during the follow-up.
The main strength of this descriptive study is the large and comprehensive (at the French level) sample provided by the SNDS claims database: we were able to include every initiating and prevalent TNF-alpha inhibitor user within the period of biosimilar products reimbursement, and describe the population. This study has nevertheless some limitations. Due to the medico-administrative nature of the SNDS, we had to use algorithms and proxies in particular to assess the patient pathologies. However, apart from the choice between multiple pathologies, the medical background codes used in this study relied on ICD-10 and were described and used in previous studies. Besides, we could not have access to clinical or behavioral data that could explain the reasons of use of the originator product rather than the biosimilar.
Methods Data source. The SNDS covers almost the totality (> 99%) of the French population-68 million residents. Each person is identified by a unique and anonymous number. The SNDS records comprehensive outpatient (reimbursed drugs and procedures) and inpatient (expensive drugs dispensed and procedures performed during hospital stays, coded according to the common classification of medical procedures-CCAM, and discharge diagnoses coded according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, ICD-10) reimbursement information since 2006. The SNDS also contains sociodemographic information on sex, age, place of residence, vital status, and complementary universal health insurance (CSS -Complémentaire santé solidaire, a system providing free access to healthcare for people with an annual income below 50% of the poverty threshold) and quintiles of deprivation index (coded from 1 to 5, 1 being the least deprived).
Patients' status for 100% reimbursement of care related to a severe and costly long-term disease (LTD) is recorded and LTD diagnosis coded according to ICD-10. Due to the medico-administrative nature of this database, clinical information such as treatment indication is not systematically recorded. The SNDS has been extensively used to conduct pharmacoepidemiological studies, especially on the use, safety, and effectiveness of health products  [22] [23] [24] [25]. Study periods and populations. Originator and biosimilar products names, dates of reimbursement and formulations are presented in Supplementary Table  S6. Study start date was defined by biosimilar entry date in France which differed for each of the three studied TNF-alpha inhibitors (referred later as TNF-alpha inhibitors for simplicity), infliximab (ATC L04AB02), etanercept (ATC L04AB01), adalimumab (ATC L04AB04).
Indeed, when browsing the literature on environmental valuation, one can observe that most studies were conducted in relatively highincome countries. For example, in their meta-analysis on the WTP for biodiversity,  Jacobsen and Hanley (2009)  mainly identify studies from developed countries. While 112 studies of their sample have been conducted in Europe, North America, and Australia, only 33 studies have been conducted in developing countries, including Africa, Asia, and South America. The metaanalyses of  Schläpfer (2006)  and  Horowitz and McConnell (2003)  mostly refer to studies in developed countries. Kristrom and Riera (1996)  find a positive income effect on the WTP based on European datasets. By broadening the origins of the studies on environmental valuation (i. e. , by broadening the studied ranges of income as well as the environmental quality levels), negative income effects might emerge more often than is currently observed.
Feature importance analysis Relative feature impact is computed by applying the Tree SHAP (SHapley Additive explanation for treebased machine learning models) algorithm to a Gradient Boosted Regression Tree (GBRT) model (see Methods section for details). The GBRT model was trained on a time series dataset with epidemiological, meteorological, socioeconomic, environmental, and global health indicator variables, where the number of Covid-19 daily cases is the dependent variable. The model is trained on 80% of the dataset and evaluated on the remaining 20% using the Root Mean Square Error (RMSE) as performance metrics for model selection. Due to the highly skewed distribution of COVID-19 daily cases reported for the different locations under study during the evolution of the pandemic, a single training-testing partition would lead to a biased analysis.
Socioeconomic, environmental, and global health indicator variables all show minor impact, including the Oxford COVID-19 Government Response Tracker (OxCGRT) stringency index which provides information on several common policy responses by governments to the pandemic such as school closures and travel restrictions. Figure  3  shows the intensity and polarity of speci c SHAP variable values for each data point (a dot in the plot) with reference to the predicted dependent variable value (high/low daily COVID-19 cases). The red-to-blue color scale indicates magnitude (high/red vs. low/blue). Position on the horizontal axis signals polarity (negative vs. positive). The resulting analysis suggests that temperature is expected to be signi cantly negatively correlated with the target, similarly to UV-index.
Wind speed does not show a speci c polarity correlation with the target/dependent variable, despite being among the most important features for the model prediction. Rainfall appears to be negatively correlated with the target/dependent variable, but there are too few observations for high rainfall values in the dataset to properly con rm this result. PM 2. 5 levels show a weak positive correlation with COVID-19 spread, while PM 10 levels show a weak negative correlation with COVID-19 spread, although there likely external cofounding factors that may play a decisive role in this result. Absolute humidity displays a weak positive correlation, despite the expected impact according to previous studies (see Introduction). SHAP values for the epidemiological terms have been omitted from the plot to focus on the effect of the other variables.
Data sources and processing The data used in this study include epidemiological, socioeconomic, environmental, global health indicator, and meteorological variables. All population-related variables are converted to percentages of total population per country. Parameters that behave as time-invariant variables during the period of focus for this study, e. g. socioeconomic variables, were used as control variables. Epidemiological data on cumulative number of con rmed COVID-19 cases were retrieved for the period from 22 January 2020 to 11 November 2020 from two main sources: the data repository by the Johns Hopkins Center for Systems Science and Engineering 28 , and the Corona Data Scraper online data service that pulls COVID-19 Coronavirus case data from veri ed sources worldwide and adds population data on a daily basis (coronadatascraper.
com). Data from these two sources are merged to create the initial starting dataset. When available, data at the regional or state level were included, in addition to countrylevel records. Country, region, and state aggregations were selected so as to achieve location records with comparable population size. We derive the number of daily registered COVID-19 cases by differencing entries in the initial dataset. Inconsistent data points (e. g. negative values) and records reporting less than 10 cases were removed. The time of exposure to the pandemic for each country was calculated as the cumulative temporal distance from the rst registered case in the country. Socioeconomic data include demographic information, technology adoption rates, and Gross Domestic Product per-capita (GDPP). Socioeconomic parameters were used as control variables as they change during the time period considered for this study.
Demographic, population density and population age data were derived from the 2019 Population Division dataset compiled by the Department of Economic and Social Affairs of the United Nation (UNDESA)  29  and the World Bank indicators database 30. Information for locations not included in the UNDESA dataset were retrieved online directly from national o cial sources. Rates of internet users, subscribers to mobile telephony services, and the number of secure Internet servers were retrieved from the World Bank indicators database. These technology adoption variables are used as proxies for the capacity of different countries to provide smart-work environments under lockdown restrictions in terms of ease of implementation of smart-working initiatives and keep the national population informed about the development of the pandemic.
GDPP data at constant price purchasing-power parity were sourced from the International Monetary Fund's World Economic Outlook Database  31. Environmental indicators retrieved from the World Bank indicators database include populationweighted exposure to ambient PM2. 5 pollution, carbon dioxide, methane, nitrous oxide emissions, and greenhouse gas emissions. These variables were used as indicators of the degree of pollution of each country, on the assumption that long-term exposure to pollutants may increase the risk of contracting COVID-19. Environmental indicators were also used as control variables. Health indicators include the Global Health Security (GHS) index, diabetes prevalence and number of hospital beds for both acute and chronic care, and the Oxford COVID-19 Government Response Tracker (OxCGRT) stringency index.
GHS providing a country-level score of health security, was used as proxy variable for a country's capability to prevent and mitigate infectious diseases. For the purpose of this study, only the "detect" category was used, which focuses on the country readiness to promptly identify and report disease outbreaks of potential international concern  32. Health indicators relative to diabetes prevalence and number of hospital beds for both acute and chronic care were retrieved from the World Bank indicators database. These variables serve as proxies for population health status and public health preparedness. Differences in intervention responses by governments to mitigate the pandemic are accounted for in terms of the Oxford COVID-19 Government Response Tracker (OxCGRT) stringency index  33. The OxCGRT stringency index includes information on containment and closure policies, such as school closures and restrictions in movement, limits on private gatherings, and full lockdowns.
Meteorological variables were obtained from two main sources: The Global Forecast System (GFS), a weather forecast model produced by the National Centers for Environmental Prediction, and the Copernicus Atmosphere Monitoring Service (CAMS) with speci c reference to the McClear Clear-Sky Irradiation model. Data obtained through GFS include daily averages, at 0. 25° spatial resolution, of temperature and relative humidity at 2 m above ground, pressure at ground level, wind speed at 10 m above ground, and rainfall. Temperature is combined with relative humidity to derive measures of absolute humidity  34. Data obtained from CAMS include the UV biologically effective dose rate (UVBED) in W/m 2 , and Particulate Matter (PM) concentrations (PM2. 5 and PM10) for 3-hour periods at a spatial resolution of 40 km.
For each reference point, we derived the geographical centroid and used the corresponding latitude and longitude coordinates to query the climatic information from GFS and CAMS through Transvalor's SoDa data service (http://www. soda-pro. com/). After merging all the different sources, the resulting dataset includes data on 180 countries covering a total population of 7,505,800,179 (96% of word population) and 46,906,240 con rmed COVID-19 cases (97. 7% of worldwide cases). Data for 28 of these countries are detailed in the dataset at a state or regional level for the available periods (see additional information in appendix for the detailed list). We consider only country-level epidemiological data for the remaining 152 countries, even if regional-level data are available from coronadatascraper.
com, to maintain a certain level of minimum comparison between the locations under study in terms of overall population size. Information on the enforcement of policy responses to COVID-19 regarding the closure of international borders and the implementation of lockdown or curfew measures (or similar policies restricting the movement of the population within the country borders) were collected from a variety of in-country data sources, including government public health websites, regional newspaper articles and crowd-sourced information on Wikipedia when veri able sources were available.
Machine learning modelling strategy The objective of machine learning modeling is to compute the relative feature impact of factors contributing to COVID-19. Feature impact is computed by applying the Tree SHAP algorithm to a Gradient Boosted Regression Tree (GBRT) model. GBRT is an additive stochastic model that combines multiple sequentially connected weak learners (regression trees) in way that each new learner ts to the residuals from the previous step to optimize the overall predictive performance  42. The resulting model can describe multiple nonlinear interaction and partial dependency with su cient exibility, very high predictive accuracy, and robustness to missing data and outliers. The study uses the open source xgboost Python library as which provides a highly e cient, exible, and portable implementation of GBRT  43.
Survey design and data collection This study uses data from a discrete choice experiment intended to elicit Pipiripau Basin landowners' willingness-to-accept compensation for reducing the use of agrochemicals. Since our Choice Experiment (CE) study of the valuation of landowners' preferences for water resource protection is based on the Water Producer Programme, we began first by organising meetings with representatives of the programme with the aim of identifying which and how many properties had already signed a contract. Following  Louviere et al. (2000)  and  Street et al. (2005)  , we applied experimental design techniques to choose the option sets that yield maximum information about interviewee preferences. The attributes and their levels were combined into choice tasks using Ngene software (ChoiceMetrics 2014).
Heterogeneity of the preferences Given the complexity of the results in Table  4  , we summarised the utility parameters for four profiles to illustrate the heterogeneity of the preferences. These four profiles where chosen by identifying the most frequent profiles in our database, and we created them based on key socio-demographic characteristics ( Table  3  ). For each profile, we calculated the marginal attribute utility. As an example Table  6  presents in details how the marginal utility parameters were calculated for profile 1 11  : The marginal utility represents how much utility is gained or lost by landowners as a result of the increase or decrease of one unit of the contract feature. For the four profiles the marginal utilities are presented in Figs. 3  and 4. In Figure  3  and 4 , we see a clear difference between the profiles since there are considerable differences in terms of estimated marginal utilities from one profile to another.
Latent class model We suspect that distinct subgroups or categories of individuals exist and a latent class model was used to model unobserved heterogeneity by identifying classes of landowners with similar preferences. Landowner and farm characteristics are used as predictors for class membership. In contrast to the mixed logit model, in the latent class model, we used continuous variables for pesticide use reduction and contract length, i. e. linear effect of reduction level and contract length within a group, as our main objective is here to identify groups with different contract preferences. The results based on the latent class model confirm that there is significant heterogeneity in the sampled landowners. We show the results of a model with three classes in Table  7  because this model described the data best.
Acknowledgements This work was supported by a grant overseen by the  French National Research Agency (ANR)  as part of the  "Investissements d'Avenir " program  ( ANR-11-LABX-0002-01 ,  Lab of Excellence ARBRE)  and  The Brazilian National Council for Scientific and Technological Development (CNPq). We thank  NOBEL  project " Novel business models and mechanisms for the sustainable supply of and payment for forest ecosystem services ", under the umbrella of  ERA-NET Cofund ForestValue  ( Horizon 2020 research and innovation programme  grant agreement N  • 773324/ANR-19-SUM2-0001-04 ).
We measure host country inclination on the basis of the level of empathy and general sentiment of a particular host country vis-à-vis the home country as reflected in news articles and reported by the Global Database on Events, Location and Tone (GDELT). We identify firms' political capabilities through the membership of current or former politicians and senior government officials on a firm's board of directors  (El Nayal et al. , 2021). We follow prior location choice literature in relying on conditional logit models and use the interactions of host country inclination and political capabilities with the indicator of the strength of a host country IPR regulations to test our hypotheses. We find empirical support for our predictions. This paper contributes to the literatures on foreign R&D investments, non-market strategies and innovation policy.
Data and methods We test our hypotheses on a sample of foreign R&D investments made during the 2003-2016 period by listed firms originating from developed countries. We focus on firms from 14 OECD countries 1 where disclosure requirements are stringent enough for us to reliably collect information on firms' board composition and the occupational backgrounds of their members, information which, as explained below, will be used to capture firm political capabilities. We limit our study to those firms that were among the top 50 market capitalizations in their home country at the time of the study. This resulted in an initial population of 1061 firms. We then identified those firms that had carried out at least one new overseas R&D greenfield investment during our period of study, thus obtaining a final sample of 163 firms, which collectively carried out 1341 such investments.
We limit our sample to new investments because decisions to expand existing operations may follow a different logic than that of a new R&D investment. We focus on greenfield investments because the location of such investments is likely to be made on the basis of those factors that are central to our theorizing. In contrast, choosing the location of acquisitions and joint ventures is conditional on target availability as well as other financial and legal considerations (e. g. Albino-Pimentel et al. , 2018; Alcacer and Chung, 2007). Our main data source on these foreign greenfield investments is the Financial Times fDiMarkets database, which provides detailed information on firms' foreign greenfield investments beginning in 2003. Given our research setting, which focuses on foreign investments aiming to produce innovation, we follow prior literature and focus on those investments labeled as "Research and Development" in the fDiMarkets database  (Castellani and Lavoratori, 2020).