dataesr/scientific-paragraphs-categorization huggingface dataset:
We then use a gradient-boosted regression tree, a flexible supervised machine learning method, to extend the consumption data to an additional set of 129 countries (for a total of 189) and 27 additional sectors (for a total of 29). Our model predicts the yearly consumption of a particular digital product brand in a country (e. g. , consumption of Google Drive in Chile during 2021). The model's features are motivated by gravity models of trade  45, 46  and include brand-level variables, such as the total revenues of the brand (across all countries), and the total consumption of all brands that are in the same sector (e. g. , all app revenues or all games revenues across all countries) (see Materials and Methods and SI Section 3. 1. and 3. 2. ). We also include features that describe the relationship between the country where the product was developed and the country where the product was consumed, such as shared language, borders, common colonizers, the geographic distance between these countries, their respective size in terms of GDP, and their ICT capacities.
To reduce the potential limitation of this assumption, we associate our estimates with upper and lower bounds generated by calculating the 95% confidence intervals for the proportion of domestic consumption of a digital product. This dataset, however, does not come free of limitations. One key problem is the assignment of corporate revenues to countries, since digital firms sometimes take legal residence in economies with favorable tax regimes (e. g. Cayman Islands, Luxembourg)  [49] [50] [51] [52]. In our paper we provide estimates based on two assignment criteria. In the main text, we leave revenues assigned to subsidiaries or legal entities as provided in the data (including tax heavens). This provides estimates that may be relevant to those interested in a fiscal view of the data. In the supplementary information SI Section 4 we also provide estimates assigning all revenues to a company's headquarter, which may be better for those interested in the geography of digital production  53.
To test this hypothesis, we conduct two robustness checks in the SI Section 7. First, we compare the concentration of trade in digital products with the concentration of physical trade in each section of the Harmonized System (HS) (each involving a few dozen products). Second, we perform a simulation where we randomly select the same number of physical products than the ones available in our dataset of digital products. In both cases we find that digital products exhibit a significantly higher concentration of exports, indicating that this is not a consequence of simply considering a smaller number of sectors. The spatial concentration patterns provide at best an incomplete picture of the networks of global trade. So next, we compare the structure of digital and physical product trade using network visualizations (Figures  3 A-C ).
We explore the twin transition by studying the relationship between decoupling of growth and emissions for a restricted sample of 40 high-income economies (positive GDP per capita growth and negative emissions per capita change, see SI Section 8. 1. for more details about our working definition of decoupling). We use high-income countries as defined by the World Bank (GDP per capita above USD 13 205), to reduce potential endogeneity issues that may arise since high-income economies are more likely to both, decouple and trade more. In SI Section 8. 2. , however, we show similar results for the full dataset. We start by dividing countries into those that have and have not decoupled growth from emissions between 2016 and 2019, using both production and consumption emission estimates from the Global Carbon Budget  65.
We then calculate the digital product and physical exports trends for these two groups. In both cases (Figure  4  C), we find that decoupled high-income economies have larger digital product export sectors compared to non-decoupled economies. In the case of economies that decoupled their production emissions, digital product exports are growing significantly faster. We find no difference, however, in physical trade, which is flat for both groups. These results suggest that decoupling emissions from growth is correlated with trade in digital products and that digitization and sustainable development might be a correlated phenomenon  20. Finally, we use our dataset to correct estimates of economic complexity, measures of economic structure used frequently in economic development studies because of their ability to explain international variations in economic growth, inequality, and emissions  [21] [22] [23] [24].
The idea is that economies engaged in more sophisticated activities can pay higher wages, produce more output per unit of emissions  39, 40  , and develop more inclusive institutions  66. As of today, the most widely used metrics of economic complexity rely on physical export data, and thus, miss key information about digital activities. We revise estimates of economic complexity by combining our digital product exports estimates by sector with physical export data using the HS4 product categorization (1200+ categories). We use this data to estimate the Economic Complexity Index (ECI) and the Product Complexity Index (PCI) of each sector and economy for 2021 (the ECI of a country is the average PCI of its exports. Both ECI and PCI have a mean of 0 and a standard deviation of 1, for more details see SI Section 9.
Third, we found that while trade in digital products represents a relatively small fraction of the global economy, it can significantly change estimates of trade balances for net digital product exporters and importers. Fourth, we showed a correlation between digital product exports and emissions decoupling, suggesting that digitalization and sustainability could be interlinked, as suggested by the twin transition hypothesis  20. Fifth and finally, we showed that digital sectors could improve metrics of economic complexity  23, 24  , revising upwards the complexity estimates of digital exporters such as Ireland, Australia, and the United States. But our dataset is also subject to important limitations. First, our data is not fully comprehensive. Our estimates are limited to the largest 8,500+ companies and the most traded digital sectors.
They do not include direct transactions among private individuals (e. g. a programmer in India selling an app development service to a client in the UK). Second, our estimates are conservative and provide only a lower bound for digital product trade volumes. That is, we assumed as little trade as possible by maximizing observable domestic consumption. Third, we are unable to track transactions between parent companies and their subsidiaries. To accommodate this limitation, we provide estimates based on two assignment rules: based on the location of each subsidiary or of the parent company. Both assignment rules, however, are not ideal, since the location of subsidiaries respond to both, local knowledge pools and tax incentives. Fourth, because we use a gravity model to produce our estimates, we cannot study the gravity structure of trade in digital products (since gravity factors such as distance are in input for our dataset).
Materials and Methods To create our estimates for bilateral trade we collect data on corporate revenues, country consumption patterns (both in USD), and couple them with machine learning and optimal transport methods. With the corporate revenues we identify the origin countries of digital production and the volume of production per country, whereas with the country consumption patterns we discover the consumption volume per country and the consumption destinations. Machine learning helps us augment missing country and digital products data. Finally, transport methods aid us in allocating the revenues to consumption patterns in an optimal way such that the distance between export origin and consumption destination is minimized. The physical trade data that used throughout the paper was taken from the Observatory of Economic Complexity (oec.
Statista (https://www. statista. com/) helps resolve this limitation by providing data on the revenue structure by parent firm. We use this source to extract the digital product sectors in which the firms are involved and to distribute the firms' revenue across these sectors (assuming that subsidiaries have the same revenue structure as their parents). Through Statista, we identify that the firms covered in our dataset are involved in 27 digital product and service sectors. See SI Section 1 for more details about the sectors. Due to constraints in the consumption patterns data (described in the following paragraph), we only collect firm revenues for the period between 2016 and 2021.
Consumption Data: We collect consumption data from a market intelligence company called AppMagic. 67  These data include detailed consumption patterns for two digital sectors (mobile games and applications) across 60 countries for the years between 2016 and 2021. The data cover consumption patterns on applications and mobile games downloaded from the Apple App Store and Google Play Store, representing most of the mobile application market. These digital products are distributed across 4,812 firms, thus extending our firm revenue coverage to 8,530. In total, by combining the consumption patterns and corporate revenue data, we have information about the revenues of 8,530 firms producing 9,446 products across 29 digital sectors and the consumption of 60 countries in 2 digital sectors for each year between 2016 and 2021 (see SI Section 2 for summary statistics of this data).
First, we include digital revenue data taken from the same sources (Orbis, Statista, and AppMagic) that were described in the Revenue data and Consumption data paragraphs. We use this data to create three input features describing the size of the product, the exporter, and the size of the digital sector: 1) the total revenue of a particular product or service across the world (in USD), 2) the total revenue of a country across all digital sectors, and 3) the total world consumption of all products belonging to the same sector (in USD). Second, we collect data on features that describe the relations between the country where the product was created and the country that is the destination of the consumption. These are 11 features capturing factors such as common official/unofficial language, colonial and political relationships, geographical proximity, and the regional location of the countries.
We collect these data from Centre d'Etudes Prospectives et d'Informations Internationales (http://www. cepii. fr/). Third, we also collect data on the GDP (in PPP constant 2017 USD) of the countries where the product was initially introduced and the country destination of consumption and generate two input features. These features help us capture the potential market size effect of the initial market of the product and the destination market. The data for these features are taken from the World Bank's World Development Indicators (wdi. worldbank. org). Fourth, we include features that describe the ICT potential of both the country of origin of the parent company of the product and the country where the product is consumed and generate six features. For this, we collect data on the share of the population having a fixed broadband connection (2 features), the share of population having mobile broadband connection (2 features), and the share of population using the internet (2 features) from the International Telecommunications Union (www.
itu. int/en). origin using the leave-one-product scheme. This improves upon a baseline linear regression model which has a limited predictive power (an R-squared of 0. 52 in the leave-one-country-out and 0. 32 in the leave-one-product-out scheme). In SI Section 3. 4. we provide more details about the predictive performance of the model using various performance metrics. To produce the final estimates, we pool the 2021 data from all 60 countries and products. We train a single model by using the leave-one-product technique to tune the model hyperparameters (since it has a more restrictive performance, see SI Section 3. 5. for detailed results). This model maps the feature vectors to estimates for the log of digital product or service consumption in a country. We then pass the feature vector for each country-product pair discovered in the Orbis and Statista dataset to produce an estimate for log of the consumption pattern.
Acknowledgements We acknowledge the support of the  Artificial and Natural Intelligence Toulouse Institute -Institut 3iA ,  ANITI ,  ANR-19-PI3A-0004  and the  Collective Learning Data Lab ERA  chair LearnData2023 Grant agreement ID:  101086712  & IAST funding from the  French National Research Agency (ANR)  under grant  ANR-17-EURE-0010  ( Investissements d'Avenir program ).
EPICOR (NCT01171404) and EPICOR Asia (NCT01361386) were prospective, international, observational, real-world practice cohort studies comprising consecutive patients hospitalized for an ACS, who survived to hospital discharge. The study cohort comprised 23,489 patients who survived after hospitalization with an ACS (11,559 with STEMI [49. 2%]; 11,930 with NSTEMI/UA [50. 8%]), enrolled from 770 hospitals in 28 countries across Europe and Latin America, and Asia (EPICOR n = 10,567; EPICOR Asia n = 12,922). Full details have been published previously. 23 , 24 The EPICOR and EPICOR Asia studies were funded by As-traZeneca. Informed consent was obtained from each patient and the study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki. The underlying study population were patients with and without SMuRFs across the whole ACS spectrum (unstable angina [UA], NSTEMI, and STEMI).
Acknowledgments : The authors extend their appreciation to the  Deanship of Scientific Research, King Saud University  for funding through  Vice Deanship of Scientific Research Chairs ; Research Chair of Financial and Actuarial Studies. Preprints (www. preprints. org) | NOT PEER -REVIEWED | Posted: 1 June 2023  doi: 10. 20944/preprints202306. 0043. v1.
Using this rich dataset, we document the following results. First, from the decision-making perspective of investors, we show that startups that planned to list at the time of their campaigns raised substantially more money than those that did not. The difference is approximately 150 %, which is economically large. However, there is a substantial difference in terms of timing. Listing plan announcements led to an over 300 % increase in fundraising during the first two years of the secondary market's operation (we obtain similar results with slightly longer or shorter periods); however, the effect decreased or even disappeared afterward. The difference is similarly distinct for the number of investors. We perform several robustness checks to ensure that this striking result is not driven by outliers or alternative factors.
Data Our initial sample includes all equity crowdfunding campaigns, both successful and unsuccessful, conducted via Invesdor from its inception in May 2012 to March 2021. We exclude any campaigns that are not pure equity crowdfunding campaigns, namely, private campaigns targeted to restricted groups of people, initial public offerings, and bond offerings. Furthermore, we exclude campaigns that did not raise any money, as they are unlikely to be seriously executed campaigns: at the very least, the entrepreneurs themselves or individuals from their close networks are expected to invest to give their campaigns initial momentum. Thus, the final main sample includes 287 campaigns conducted by 244 different companies. In our investment-level analyses, we include only transactions deemed to reflect real investment intentions, excluding transactions that were cancelled, rejected, or left unpaid.
Methodology for identification strategy Our aim is to test the impact of a secondary market on startups' fundraising capacity. Our identification strategy relies on estimating the impact of the announcement of a listing plan by a company on its fundraising capacity. This helps answer the question of whether announcing a listing plan leads to an increase in the money raised during an equity crowdfunding campaign. We further perform similar analyses on the number of investors and the likelihood of achieving the funding goal. Indeed, our data include not only campaign-level information but also investment-and investor-level information. The latter enables us to run our analysis at the investment level and thus examine the impact of investor characteristics on individual investments. In addition, we use an instrumental variables approach to address the possibility that a listing plan is endogenous.
Platform database Investor in capital Indicator equal to 1 if the investor is located in a capital city and 0 otherwise. Platform database of these companies had committed to listing during their campaigns, which suggests that many companies do not pre-commit to a listing during their campaigns despite their possible intentions to list or that they only decide whether to list afterward, possibly prompted by shareholders' requests. One possible reason is that listing may have adverse effects if shares are traded at very low prices; thus, companies first want to see the outcome of their equity crowdfunding campaigns to determine the overall investor interest in their shares. Finally, Table  4  presents the summary statistics of the sampled investments. Each observation represents an investment made by a crowd investor.
The larger shareholder base of more successful campaigns may also induce the corresponding companies to list even if they did not originally plan to. Most of the control variables do not affect the probability of listing; however, B2C companies are more likely to list than B2B companies and foreign companies are less likely to become listed. As a final piece of evidence, we present data about whether the companies with successful campaigns were still active as of May 2021 (or whether they were bankrupt, in liquidation, or dissolved) and whether they had received subsequent equity financing from a financial player, from crowd investors via another equity crowdfunding campaign, or from a strategic buyer. It is beyond our scope to infer any causality in relation to listing, but the data highlight a positive correlation between becoming listed and these other variables (Table  12 ).
Acknowledgements The authors wish to thank  Guillaume Andrieu ,  Daniel Blaseg ,  Helen Bollaert ,  Alexander Groh ,  Lars Hornuf ,  Thomas Lambert ,  Benjamin Le Pendeven ,  Florencio Lopez-de-Silanes ,  Jukka Luoma ,  Markku Maula ,  Moritz Renner , and  Mikko Rönkkö ; the conference and seminar participants of the 3rd  Toronto Fintech Conference (Ivey Business School, Toronto) , the 4th  European Alternative Finance Research Conference (European Center for Alternative Finance at Utrecht University) , the  Cryptocurrency Research Conference  ( ICMA Center at the University of Reading ), and the  MaCCI Conference on "Competition and the Regulation of Financial Innovation" (ZEW Mannheim) ;  Audencia Business School, SKEMA Business School , and  Aalto University ; and Editor  Martin Kenney  and the three anonymous reviewers for their helpful comments and suggestions.
within a year) after the campaign; such campaigns are often positioned as pre-IPO rounds. Already listed: The company was already listed at the time of the campaign (as a consequence of an earlier equity crowdfunding campaign). Listed Indicator equal to 1 if the Websites of secondary company is or has been listed markets operated in the on a secondary market and home countries of the 0 otherwise. Note that a sample companies listing does not presuppose that trades took place. Companies were listed on Privanet, First North Finland, First North Sweden, Nasdaq Helsinki, NGM's Nordic SME list, and the Direct Market segment of the Vienna Stock Exchange. Previous Number of successful Platform database retractions campaigns that ended before Campaign text extracts the respective campaign that sourced from the focal had a listing plan but did not platform list.
Campaign-level variables: campaign outcomes Amount raised Total amount raised (in Platform database euros) during the campaign. Regression models use the natural logarithm because relative changes in the amount raised are more relevant in this context than absolute changes. Number of Number of investors who Platform database investors participated in the campaign via the platform. Regression models use the natural logarithm because relative changes in the number of investors are more relevant in this context than absolute changes. Campaign Indicator equal to 1 if the Platform database successful campaign reached its minimum funding target and thus was successful and (continued on next page).
Measure of company's target (or maximum funding preparedness. Regression target if the campaign has no models use the natural minimum target). logarithm. Ratio of funding Ratio of the average funding Platform database Team size Number of named individual Campaign text extracts ratios (X ratios of campaigns with and human team members sourced from the focal months) without a listing plan. presented on the campaign platform Includes campaigns that main page. ended in the last X (6, 9, or Previous success Indicator equal to 1 if the Platform database 12) months before the end of company has previously run Finnish equity crowdfunding the respective campaign. a successful equity platforms' websites Valued 0 if the timeframe crowdfunding campaign and does not include campaigns 0 otherwise. with a listing plan.
A value Social media Number of times the SharedCount larger than 1 implies that the activity campaign page was shared, funding ratios of campaigns commented, or reacted to on with a listing plan were on social media (Facebook and average larger than those of Pinterest). campaigns without a plan. Simultaneous Number of campaigns that Platform database Entrepreneur's Share of the total amount Platform database campaigns were simultaneously ongoing networks raised that was collected via on the platform during the external investments, which campaign. Average daily usually represents larger number of simultaneous investments from the campaigns. entrepreneur's private Foreign campaign Indicator equal to 1 if the Platform database networks. For example, 0. 05 company is located in a means that 5 % of the total different country than the was raised via private focal platform and the focal networks.
secondary market (i. e. , if the company is located outside Campaign-level variables: campaign content and specifications Funding target Minimum funding target (in euros) of the campaign (or Platform database dummies Campaign start Finland). the campaign start year: Indicator variables based on Platform database Orientation Company age Professional investor Free float offered Duration maximum target if the campaign has no separate minimum target, as with IPOs). Indicator equal to 1 if the company mostly offers its products directly to consumers (B2C), and 0 if the company mostly sells to businesses or other organizations (B2B). Company age (in years) at the time the campaign started. Indicator equal to 1 if the company had secured funding from a business angel or a venture capital fund before or during the campaign and 0 otherwise.
Fraction of equity given out by the company if the campaign were to reach its minimum target (or maximum target if the campaign has no minimum target). Campaign duration (in days). actual campaign duration, This variable reflects the Campaign text extracts sourced from the focal platform Orbis database Platform database Campaign text extracts sourced from the focal platform Platform database Platform database Before or after competition End after cutoff Valuation Campaign updates generating Revenue Campaign start 2012-14 (reference category) Campaign start 2015-16 Campaign start 2017-18 Campaign start 2019-2021 Indicator equal to 1 if the campaign started before the opening or after the closing of a competing equity crowdfunding platform operated by the focal secondary market provider and 0 otherwise.