dataesr/scientific-paragraphs-categorization huggingface dataset:
background. 9  Conversely, women seem to be more vulnerable when the impact of socioeconomic status on health-care access is assessed. 10  The significance of being treated in a high-volume expert centre in oncology is widely recognised, with numerous studies demonstrating that higher surgical volumes correlate with reduced complication rates, enhanced oncological outcomes, and decreased costs. 11, 12  Additionally, treatment at high-volume centres increases the likelihood of patients receiving care in accordance with established guidelines. 13  Despite the welldocumented advantages of high-volume expert centres in oncology, no studies have yet investigated potential disparities in patient access to specialised cancer care facilities based on gender. In light of these observations, our study aims to provide a clear and comprehensive insight into the impact of gender and socio-environmental indicators on health-care access in oncology on an exhaustive nationwide database.
Patient data and selection Data were obtained from the French national health insurance system database, which covers 98. 8% of the 67 million inhabitants of France. All the medical and administrative information relating to the reimbursement of French citizens for health-care expenses in hospitals is collected and aggregated within this system. The data recorded include demographic data (gender, date of birth, geographic code of the area of residence), hospital discharge reports (diagnoses, medical and surgical procedures), and all long-term illness records (diagnosis codes and date of disease onset). We selected patients diagnosed with solid invasive cancers between the 1st January 2018 and the 31st December 2019 (Appendix 1). We ensured that only incident cases were identified by excluding patients with an existing cancer diagnosis in 2016 and 2017 from the study.
Patient and tumour characteristics The clinical features assessed in this study included gender, age, comorbid conditions and type of cancer. The French national health insurance system database traditionally records sex as a binary attribute (male/female), reflecting biological distinctions established at birth and reflected on identification documents. For the purpose of our study,  14  we choose to use the nomenclature 'gender' to encompass not only the biological distinctions but also the social and cultural differences that typically associate with the categories of men and women. Non-binary and LGBTQ+ populations could not be assessed within the scope of this study. Age at diagnosis was analysed as a continuous variable. Comorbid conditions were identified in the database from (i) the main, relative and associated diagnoses made during hospital stays and (ii) the medical and surgical procedures undergone during hospital stays (Appendix 3).
Absolute and relative cancer care expertise indexes The French national health insurance system assigns a unique facility code to each health-care centre where patients receive oncological care. This code helps determine the centre's volume, defined as the annual number of patients treated for each cancer type at the centre. This outcome-centre specific volume for individual cancer types-is not novel and is a validated metric extensively employed in prior studies assessing quality of care and the influence of volume on varied cancer types. 12, 15, 16  Recognising that cancer care expertise extends beyond volume, we gathered data from the 2019 French Annual Health Facilities Statistics (SAE) to identify centres specialised in oncology, radiotherapy, and multi-disciplinary surgery. The SAE database is a comprehensive, mandatory, and publicly available survey of all public and private hospitals in France.
Conducted annually, the survey provides information on hospitals' activities, services, and staff. We selected 35 variables from the SAE, divided into four categories: oncology, radiotherapy, multidisciplinary surgery and other related activities (e. g. , palliative care, medication circuit). By employing a Factor Analysis of Mixed Data (FAMD), we calculated an oncological and surgical expertise score ranging from 0 (low expertise) to 1 (high expertise) for each centre (Appendix 4). The absolute cancer care expertise index was determined by the specific volume for each cancer type, weighted by the centre's oncological and surgical expertise score. While the volume specific to each cancer type underscores the centre's specialisation and depth of experience with individual cancers, the broader oncological and surgical expertise score, derived from a broader spectrum of data including multidisciplinary surgery and palliative care, reflects the centre's comprehensive capabilities across surgical and oncological disciplines.
Socio-environmental indices Socio-environmental data for the area of residence of the patients were collected from three official French governmental open-data websites: the National Institute of Statistics and Economic Studies (www. INSEE. fr), the Observatory of Territories (www. observatoire-desterritoires. gouv. fr) and the Open Platform for French Public Data (www. data. gouv. fr). We recovered data for 71 variables organised into eight categories: inaccessibility to public transport, gender-related wage disparities, economic deprivation (e. g. , poverty rate, median income), social isolation (e. g. , rate of widows or elderly people living alone), unemployment, educational barriers, familial hardship (e. g. , rate of large or singleparent families) and insecurity (e. g. , homicide rate).
2C ). Comorbidity profiles by age differed significantly by gender, with notably higher frequencies and numbers of comorbid conditions in men than in women (Fig. 2D ). for men, p < 0. 0001), while men were more likely to be treated at higher volume centres for liver and biliary tract cancer (50 [17-150] compared to 31 [13-117] for women, p < 0. 0001, Appendix 8B). Among the included facilities, 690 (49%) lacked authorisation for cancer surgery, 744 (52%) did not offer chemotherapy services, and 1293 (91%) did not provide radiotherapy activities (Appendix 8C). Complete data from the SAE database were missing for 29 (2%) facilities. The centres' median oncological expertise score was 0. 3, with a range of 0. 1 to 0. 8 (Appendix 8D). The median distance to the cancer centre was 21 [8-45] kilometres for both genders (Appendix 8E).
The data reveals that, except for unemployment, all calculated socio-environmental factors were negatively associated with cancer care expertise indexes. However, this association was more pronounced for women. The data underscore that inaccessibility to public transport had the most substantial impact on health-care access. (C) Interaction between age, number of comorbidities, and gender on the absolute cancer care expertise index, and (D) on the relative cancer care expertise index. As age and the number of comorbidities increased, a more rapid shift towards treatment in lower-volume centres was observed among women compared to men. comorbid conditions and health-care access in patients with cancer. Comorbid conditions have a disproportionately negative impact on disadvantaged populations  31, 32  and influence cancer diagnosis, tumour biology, and subsequent treatment choices.
The oncological management of elderly patients with frailties can be more challenging and may necessitate the expertise offered by high-volume centres. 39, 40   While this study offers valuable insights into gender disparities in health-care access for oncology patients, it is critical to address the limitations concerning the representation of gender. In the French national health insurance system database, gender is traditionally recorded as a binary attribute (male/female) based on biological sex. Our study has used this data and referred to it under the nomenclature 'gender' to broadly represent the societal roles and cultural differences associated with men and women. However, this binary categorisation inherently fails to capture the diversity in gender identities that fall outside the traditional male/female classification, such as non-binary individuals or members of the LGBTQ+ community.
Study population The present study used observational data from the NutriNet-Santé study. The NutriNet-Santé study is an Internet-based cohort launched in May 2009  24. Its purpose is to study the determinants of diets, nutritional status, and physical activity as well as their associations with health. The participants, recruited on a voluntary basis, are adults living in France with an access to internet. Participants have to complete annual or biannual questionnaires on socioeconomic status, lifestyle, anthropometry, dietary intake and physical activity. Regularly, specific questionnaires are proposed. Gender, occupational status, income, place of residence, physical activity, and smoking habits are self-reported using validated questionnaires  25. The NutriNet-Santé study is in line with the principles of the Helsinki Declaration  26  and the protocol has been approved by both the INSERM Ethical Evaluation Committee (CEEI) (no.
Nutritional quality data Three dietary indexes were computed. The PANDiet (Diet Quality Index based on the Probability of Adequate Nutrient Intake) is a nutritional adequacy score based on the nutritional references values  32, 33. The PNNS-GS2 (Programme National Nutrition Santé-Guidelines Score 2) measures the adherence of individuals to the French dietary guidelines established by the High Council of Public Health in 2017  34. The cDQI (Comprehensive Diet Quality Index) aims to assess the quality of plant and animal foods consumed  35. Further details are provided in Supplemental Material 2.
Economic data The economic data used were participants' monthly income, and their estimated food expenditures for their whole diet and each food group. Participants' income was collected as part of the socio-economic status questionnaire, where each participant provided the income class corresponding to his/her monthly income. Income per consumption unit (C. U) were estimated using household composition and age of family members according to the INSEE procedure 36. In the NutriNet-Santé study, the monetary cost of the diet (€/d) was calculated for each participant using prices (€/g) from several databases. Further details are provided in Supplemental Material 4.
Construction of the protein-source-typology and description of clusters The contribution (in %) to total protein intake of the 23 food groups was calculated for each individual, to focus on the sources independently of the total intake. The typology aiming to identify groups of individuals with similar protein sources was built using a two-step procedure. First, a Principal Component Analysis  37  was applied on the 23 protein contributing food groups (the list is available on Fig. 1 ). This multivariate data analysis method allowed to reduce the initial range of information by maximizing the variance. Nine dimensions were retained according to Kaiser criterion (eigenvalues > 1). Then, on the basis of the retained dimensions, an Ascending Hierarchical Classification (AHC) was performed with data preprocessing using the K-means algorithm reiterated 100 times.
Economic analysis The objective was to analyze both food and protein expenditure structure across clusters. The economic analysis included 27,244 of the 29,210 participants, for whom there were no missing income data (since the question was optional). The monthly income variable, modelled, as category was transformed into a numeric variable by considering the class center of the daily income category for each individual as previously done  39  and converted as euros per day. The expenditure structure analysis across clusters was conducted using a budget coefficient approach  40. This approach makes comparable the share of food expenditure between individuals with different incomes and different diets  40. To do so, we first computed for each participant, the budget coefficients of both the overall diet and the food groups, using the following formulas: where i denotes individuals and j denotes food groups.
Afterwards, non-adjusted means (SD) were computed for all the calculated budget coefficients for the whole sample, and for each cluster, means and standard error of the mean (SEM) adjusted for energy intake were estimated using ANCOVA models. Comparison between clusters was based on relative percentage values computed using standardized means as defined above. Comparison of means across clusters was performed using the ANCOVA test. Data management and statistical analyses were performed using RStudio software (RStudio, Version 1. 4. 1717, © 2009-2021 RStudio, PBC).
Acknowledgements We thank  Cédric Agaësse ,  Alexandre De-Sa  and  Rebecca Lutchia  (dietitians) (dietitian);  Thi Hong Van Duong ,  Younes Esseddik  ( IT  manager),  Régis Gatibelza ,  Jagatjit Mohinder  and  Aladi Timera  (computer scientists);  Julien Allegre ,  Nathalie Arnault ,  Laurent Bourhis ,  Nicolas Dechamp  and  Fabien Szabo de Edelenyi , PhD (supervisor) (data-manager/statisticians) for their technical contribution to the NutriNet-Santé study;  Merveille Kouam  (health event validator),  Maria Gomes  (Nutrinaute support) and  Nathalie Druesne-Pecollo , PhD (operational coordination). We thank all the volunteers of the NutriNet-Santé cohort. Nicolas Dechamp  (data-manager/ statisticians);  Merveille Kouam  (health event validator);  Maria Gomes  (participant support). Alexandre De Sa  (dietitian),  Rebecca Lutchia  (dietitian).
Funding The  NutriNet-Santé  study is funded by  French Ministry of Health and Social Affairs, Santé Publique France ,  Institut National de la Santé et de la Recherche Médicale ,  National Research Institute for Agriculture, Food and the Environment, Conservatoire National des Arts et Métiers , and  Sorbonne Paris Nord University. The  BioNutriNet  project (PI EKG) was supported by the  French National Research Agency (Agence Nationale de la Recherche)  in the context of the  2013 Programme de   Recherche Systèmes Alimentaires Durables  ( ANR-13-ALID-0001 ). The funders had no role in the study design, data collection, analysis, interpretation of data, preparation of the manuscript, and decision to submit the paper. JBr was supported by a Doctoral Funding from  National Research Institute for Agriculture, Food and the Environment (INRAE)  and  ADEME-Galilée Doctoral School.
Consistent with  Peters (2000) , the variable BP was normalised to allow the data to be mapped according to [0,1], BP ¼ S5 max ÀS5 S5 max ÀS5 min. The value of BP is higher when a firm has a higher buyer power. Seller power (SP) is calculated by the proportion of total sales of the top five customers (C5). Similar to the calculation of the variable BP, seller power is calculated as SP ¼ C5 max ÀC5 C5 max ÀC5 min. The value of SP is high when a firm has a high seller power. The control variables which are mainly considered are capital input (K/L), labour input (L), nature of firms, the classification of strategic industries, age, year, and the geographical distribution of cities. The capital input K is calculated as 10% of total assets to include depreciation  (Verspagen, 1995). Data and statistical description.
We examine the joint effects of government subsidies and various types of (supplier and buyer) market power on firm performance in strategic emerging industries using data from Chinese publicly listed firms. We collected the data sample from 2006 to 2019. We did not use data after 2020 as the data quality has been influenced by the effects of lockdown measures caused by COVID-19. We adopted the following procedures for data collection. First, data samples are selected from listed companies affiliated with manufacturing industries by considering the fact that listed manufacturing companies are more likely to be favoured by government innovation subsidies. Secondly, consistent with the document 'Classification of Strategic Emerging Industries (2018)' issued by the National Statistics Bureau of China, we were able to manually place each firm into one of the following categories in the strategic emerging sectors: digital creative, next-generation information technology, new materials, renewable energy, electric cars, biotech, energy conservation and environmental protection, as well as high-end equipment manufacturing.
Thirdly, we deleted observations where the value of employment is missing. After excluding the data of non-strategic emerging enterprises, 10835 firm-level observations of 1392 companies were selected. The distribution of firms across sectors was as follows: new materials (26. 77%), next-generation information technology (18. 95%), high-end equipment manufacturing firms (18. 65%), biotech (16. 18%), and energy conservation and environmental protection firms (10. 91%). A small percentage of companies were from renewable energy (4. 63%), electric cars (2. 32%), and the digital creative sector (1. 60%). Table  1  presents the descriptive statistics. The mean per employee enterprise R&D investment during the sample period is 1. 280 with a standard deviation of 0. 765. The labour productivity is 4.
Summary and conclusion China has evolved from a low-tech manufacturing economy to an innovation-driven economy as a result of extensive R&D investment by firms and subsidies provided by government. However, there are still several hurdles innovative activities, particularly for strategic emerging industries facing higher risks and greater technological constraints compared to firms in developed countries. Governmental subsidies can help towards decreasing the risk of innovation activities for private firms, although their usefulness in encouraging innovation is not adequately acknowledged. Based on the dataset of listed companies in strategic emerging industries in China, this paper has examined the impact of government subsidy and market power on firm performance in greater detail. Consistent with the structural model literature  (Crépon et al.
Acknowledgements This research was supported by the  National Natural Science Funding of China  (Grant number:  72102090 ),  Humanities and Social Sciences Project of Ministry of Education  (Grant number:  21YJC630112 ), and  Philosophy and Social Science Research Fund of Jiangsu  (Grant number:  2021SJA2070 ). We also thank the support from  Philosophy and Social Sciences Excellent Innovation Team Construction Foundation of Jiangsu Province  ( SJSZ2020-20 ). The usual disclaimers apply.
Despite global efforts to harmonize international trade statistics, our understanding about trade in digital products and its implications remains elusive. Here, we study five key aspects of trade in digital products by introducing a novel dataset on the exports and imports of digital products. First, we show that compared to trade in physical goods, the origin of digital products exports is more spatially concentrated. Second, we show that between 2016 and 2021 trade in digital products grew faster than physical trade, at an annualized growth rate of 20% compared to 6% for physical trade. Third, we show that trade in digital products is large enough to partly offset some important trade balance estimates, like the physical trade deficit of the United States. Fourth, we show that countries that have decoupled economic growth from greenhouse gas emissions have larger and faster growing exports in digital product sectors.
Digital goods have product like properties, such as the purchase of a video game, which includes acquiring a digital asset (e. g. , a copy of a video game) with negligible marginal cost to the producer. Digital services, such as cloud computing, on the other hand, do not involve the transfer of a digital asset, but temporary access to a digital service or infrastructure, such as a dedicated virtual machine or server, which has some marginal cost. Trade in digital products involves a wide spectrum, from pure digital goods such as standalone video game downloads, to combinations of digital goods and services, such as video streaming platforms, such as Netflix, which provide temporary access to a digital good (movie, TV show, etc. ) through a service subscription model. To improve our understanding of the growth, geography, and implications of trade in digital products, we generate a novel dataset on the international trade of digital products by combining machine learning and optimal transport techniques with data on corporate revenues and digital products consumption.
The resulting dataset provides yearly estimates (in USD) of trade in digital products for 189 countries and 29 sectors for all years between 2016 and 2021. We use this data to explore trade balances, economic decoupling, and measures of economic complexity, showing that trade in digital products adds valuable nuances to these three phenomena. Consider trade balances  [9] [10] [11] [12] [13] [14]. In 2021, the United States experienced a physical product trade deficit of USD 1. 1T (USD 1. 63T in exports and USD 2. 73T in imports)  15. An important part of this deficit (about 30%), however, is offset by the fact that the US is a net exporter of "bits" (digital products) and a net importer of "atoms" (physical products). Now consider economic decoupling: the separation of greenhouse gas emissions from economic growth  [16] [17] [18] [19] [20].
We also find trade in digital products to be growing rapidly, at an annualized rate of 20% between 2016 and 2021 (from USD 411 billion to USD 1. 02 trillion) compared to the 6% growth rate experienced by physical trade during the same period. Furthermore, we find the geography of trade in digital products to differ from that of trade in physical products, with exports concentrated in fewer origins but imports being distributed more evenly. In the remainder of the paper, we present the data and methods used to generate bilateral data on the trade of digital products for each sector, and then use this dataset to investigate the implications of trade in digital products for trade balances, emission decoupling, and economic complexity. These findings contribute to our understanding of the role of digital trade in sustainable economic development.
Estimating trade in digital products We construct a dataset of trade in digital products by combining ground truth data on the consumption of digital products for 60 countries with corporate revenue data for 8,530 digital firms. Figure  1  presents a schematic of our procedure. We use machine learning and optimal transport techniques to extrapolate this data to a total of 189 countries and 29 sectors (see Supplementary Information (SI) Section 1 for the countries and sectors covered in our dataset). First, we gather corporate revenue data (in USD) for digital products from Orbis and Statista (see Materials and Methods). Orbis is one of the largest databases on firm level data with information on 400+ million firms across the globe  42, 43. We use Orbis data to identify the largest parent firms involved in the trade of digital products (with revenues of USD 1B or more) and their subsidiaries, including important app and game developers (for a total of 8,530 firms).
We then collect yearly revenues from all the companies on this list and use Statista 44 data to decompose corporate revenues by digital product sectors. For example, we decompose the revenue of Alphabet Inc. and their subsidiaries into revenues originating in digital advertising (e. g. Google search), cloud computing (e. g. Google cloud and drive), etc. We then combine these estimates with country consumption data (in USD) from a mobile market intelligence company tracking the consumption of all applications and games downloaded from the Apple's App Store and Google's Play Store for 60 countries (SI, Table  S2 ). We merge these datasets by connecting each digital product to its country of origin and to the countries where consumption took place. See SI Section 2 for the summary statistics of these data.