dataesr/scientific-paragraphs-categorization huggingface dataset:
On average, a NUTS2 region has an area of 16310 km 2 , equivalent to a spherical radius of 40. 67 km  (Eurostat, 2011). To assign each VC investor to a NUTS2 region, we first translated the textual information on the ZIP code, city and country of the VC headquarters (provided by Thomson One) into numerical information on the latitude and longitude coordinates. This geocoding process was automated using Google Maps Geocoding API and the R command "geocode". We then used geographical data on the administrative boundaries of each NUTS2 (retrieved from Eurostat's website) to link each pair of VC coordinates with a NUTS2 identifier. Fig. 2  shows the number of new investments by the location of VC investors (at the NUTS2 level) over the 1984-2009 period. The Inner London and Île-de-France (Paris) regions stand out; they represent 21.
Independent variables: the weighted distance and the radius specifications Our independent variables assess the effect of the geographical distance between the locations of companies and prospective VC investors on companies' demand for external equity. To develop these variables, we use information from two sources. From Thomson One, we retrieved data on the number of new investments by European VC investors and their location. From VICO, we use information about the location of sample companies. We use two alternative specifications in which we model differently the impact of the distance from the location of prospective VC investors: the "weighted distance" specification and the "radius" specification. In the "weighted distance" specification, we include three variables capturing the availability of VC in the same region of the company, in the same country and abroad: VCavailability_ local i,t , VCavailability_ national i,t and VCavailability_ abroad i,t.
We coded the answers to these questions with the dummy variables manager i , serial i , MBA i and PhD_science i , respectively. We measured cash generation and leverage with the variables cashflow/ sales i,t-1 , equal to the ratio between cash flow and sales, and debt/totalassets i,t-1 , equal to the ratio between total debt and total assets. We measured capital expenditures with CAPEX/assets i,t-1 , i. e. , the variation of fixed assets with respect to the previous year divided by companies' profits. The accounting data used to compute these ratios were retrieved from Bureau Van Dijk's Amadeus dataset. The accounting variables were winsorized at the 1% level to reduce the influence of outliers. As accounting variables are unavailable for a fraction of sample companies, we impute the missing values to 0, and generate a dummy variable called d_accounting_missing i,t equal to 1 if the data has been imputed, and to 0 otherwise.
The former three variables were retrieved from Eurostat, while the latter was extracted from the VICO database and computed as the average ratio of debt on equity of all VICO companies located in a given region. Finally, variables at the national level are included. MSCI_national i,t-1 is the Morgan Stanley Capital International (MSCI) index lagged by 1 year, i. e. , it is a measure of stock market performance in each European country. Cumming et al. (2010)  find that MSCI returns accelerate the VC due diligence process, which may stimulate companies' demand for external equity. Furthermore,  Armour and Cumming (2008, 2006)  show that bankruptcy laws influence the extent to which entrepreneurs take risk, which is likely to also affect the demand for external equity. Therefore, we include the dummy variable bankruptcylaws_national c,t-1 , which is equal to 1 in country-years during which a bankrupt individual can obtain a "fresh start", indicating that bankruptcy law is relatively lenient.
The results are reported in column 1 of Table  5. Both VCavailability _ local and VCavailability _ national still have positive and significant coefficients (at the 5% level), suggesting that reverse causality between initial location and demand for external equity does not drive our results. Furthermore, we investigate whether companies re-locate after foundation to increase their probability of success in seeking VC. To tackle this issue, we study a sample of 332 companies included in the VICO database and founded in 2003 or 2004 for which we were able to track the location between foundation and 2009. Information on location at foundation and location in 2009 was retrieved using historical versions of the Orbis database (the database traces companies' locations since 2003). 90 companies out of 332 (27.
3709, p-value = 0. 7109). These results confirm that at least in the period and geographical regions under analysis, relocation to VC hubs is not a strong source of reverse causality between the local availability of VC and companies' demand for VC, and does not threaten the validity of our results. A second concern may be that our sample contains companies that did not look for external equity simply because they were unlikely to receive VC, for instance, because of their limited quality or growth orientation, regardless of their location. We believe that in our sample this issue is limited, as the VICO database was created with the specific objective of including companies that potentially would be a target for VC investment (e. g. , based on their industry, age and size), independently of whether or not they obtained VC  (Bertoni and Martí, 2011).
Unfortunately, this robustness check is not feasible because of the severe drop in observations when considering VCbacked companies only. column 4 of Table  5  and are once again robust, indicating that our results on the association between the geography of VC and companies' demand for external equity are not driven by companies that successfully obtained VC. As a third robustness check, we acknowledge that the survey-based nature of our study potentially creates non-respondent bias concerns. Respondents may be systematically different from non-respondents, and this might have an impact on our results. To tackle this issue, we use a Heckman approach to account for any response bias that can emerge from the data. Based on a dataset including all companies in the VICO database to which the 2010 survey was administered, we estimate a probit model whose dependent variable is a dummy equal to 1 for respondents and 0 for non-respondents.
For each country, we retrieve information from the World Governance Index database  (Mastruzzi et al. , 2007)  on six indexes capturing different institutional dimensions: voice and accountability, political stability, government effectiveness, regulatory quality, rule of law and control of corruption. Similarly to  Li and Zahra (2012) , we compute the level of formal institutional development in each country with a principal component analysis on the six indexes. The principal components analysis indicates that the first component accounts for 85. 14% of the total variance and is calculated as Institutional Development = [Voice Accountability × 0. 3904 + Political Stability × 0. 3643 + Government effectiveness × 0. 4245 + Regulatory Quality × 0. 4132 + Rule of law × 0. 4312 + Control of corruption × 0.
Logarithm of the GDP in the region k (NUTS2) of venture i in the year t-1. Authors' elaboration based on Thomson One and GoogleMaps Authors' elaboration based on Thomson One 8 age i,t Logarithm of venture's age. 9 manager i VICO survey 10 serial i VICO survey 11 MBA i Dummy equal to 1 if among the group of founders of the venture there were one or more VICO survey individuals who had obtained an MBA or a master degree in Economics before founding the venture. 12 PhD _ science i Dummy equal to 1 if among the group of founders of the venture there were one or more VICO survey individuals who had obtained a PhD in technical or scientific disciplines before founding the venture. 13 cashflow/sales i,t-1 Ratio between cash flow and sales, computed in the year t-1. VICO dataset 14 debt/totalassets i,t-1 Ratio between total debt and total assets, computed in the year t-1.
VICO dataset 15 CAPEX/assets i,t-1 Ratio between capital expenditures and total assets, computed in the year t-1. VICO dataset 16 patent _ stock i,t-1 Depreciated number of granted patents. Granted patents are assigned to the application year. VICO dataset We use a 0. 15 knowledge depreciation rate. 17 totalassets i,t-1 Total assets in the year t-1. VICO dataset 18 d _ accounting _ missing i,t-1 Dummy equal to 1 one the accounting information were missing. VICO dataset 19 debt _ local k,t-1 VICO dataset 20 patents _ local k,t-1 Eurostat 21 22 GDP _ local k,t-1 area _ local k,t-1 Area of the region k (NUTS2) where the company is located (km 2 ). Eurostat Eurostat 23 MSCI _ national c,t-1 MSCI index measured at the national level. The Morgan Stanley Capital International index is a Morgan Stanley measurement of stock market performance in a particular area.
dATA sourCes We used two sources of data for this study: (1) retail private market sales database and (2) the market authorisation registry, which is managed by the National Institute of Public Health (ISP). The private market sales database (database procured by the Ministry of Health of Chile to IQVIA [previously called IMS Health] in Chile) includes information collected between November 2011 and October 2016, on the volume and total private sales (in CLP) of medicines that were sold directly to consumers at the main private pharmaceutical chains of the country (private retail outlets). This database provides details on every transaction for pharmaceutical products, such as the name of product, API, pharmaceutical presentation, manufacturer, and if it requires being sold with a prescription or not (over the counter).
data analysis and presentation of results We used single-group ITS analysis to measure the effect of this policy,  13 14  without making any seasonality adjustments. We used monthly data and February 2014 as the intervention date. Considering the data from November 2011 until October 2016, we used a total of 60 data points for each time series (27 for the pre-intervention period and 33 for the post-intervention period). We calculated the total number of DDDs (volume) by adding up the number of DDDs of different presentations with the same API. These analyses were performed in Stata V. 14, using the itsa command, with the option newey. This option estimates coefficients by automatically correcting for autocorrelation and possible heteroscedasticity. 15 o present the results, we structured the ITS analyses in two levels.
BMJ Global Health For every ITS analysis, we report the following coefficients, with the 95% CI and its p value (we considered as statistical significant p values less than 0. 05) as presented: ► Level before the substitution policy ( β0 ). ► Trend before the substitution policy ( β1 ). ► Level change after the substitution policy ( β2 ). ► Trend change after the substitution policy ( β3 ). In addition to the volume analysis, we compared the ratio of the prices of branded generic BEQ alternative with the referent and analysed if this relationship changed over time. To do this, first we calculated the median price per DDD and its IQR of all referents and BEQ that contain the same API for 2011 and 2016. The prices were obtained from the same private market sales database from which we obtained volume data.
Finally, there are certain limitations to our private sector medicine data as well. IQVIA (formerly IMS Health) collects data from audits/surveys in the distribution channels of a country, and these channels may change over time and there may be private sector channels in which data are not collected. Clearly, changes in volume and price based on such supply chain audits is not actual patient consumption. Nevertheless, the major strength of these particular data in Chile is that it is indeed capturing most of the private market. 28.
Acknowledgements VW and WK are co-investigators of the evaluation of Access Accelerated, an Initiative that seeks to increase access to non-communicable disease treatments in low-income and middle-income countries. Their institution is receiving a grant to study the effects of this initiative. However, the publications of the results are not subject to control by the funding organisation, IFPMA (see also agreement http:// sites. bu. edu/ evaluatingaccess-accessaccelerated/ agreements/). CAH changed his affiliation from the  Ministry of Health of Chile  to the OECD while developing this article. The opinions expressed in the paper are the responsibility of the authors and do not necessarily reflect those of the OECD or its member countries Contributors All authors have read and approved the submission of the manuscript.
The health impacts (i. e. number of new HIV infections, people living with HIV, people on ART, AIDS-related deaths) of 20 key interventions were estimated per country and per year. For each country, these interventions were provided at coverage levels according to the two scenarios described earlier. We further assume that both scenarios will have a similar future cost function, meaning that improvements in efficiency and reductions in unit costs (e. g. with the introduction of medicines in the pool of generics) apply to both the Fast-Track and the constant-coverage scenarios. Regional estimates of the incremental benefit-cost ratio are the summation of the incremental benefits of the countries in a region divided by the summation of their incremental costs. GDP per country was taken from the IMF World Economic Outlook database until 2021 and has been projected for 2022-2030.
Acknowledgments We are grateful to  Prof. Dean T. Jamison  from the  Department of Global Health, University of Washington ,  Lisa Robinson  and  Stephen Resch  from  Harvard T. H. Chan School of Public Health, Gavin Yamey from Evidence to Policy initiative, Global Health Group ,  Peter Ghys ,  Chris Fontaine  and  José Antonio Izazola  from  UNAIDS ,  Bruno Ventelou  from  Aix-Marseille University ,  Lori Bollinger  from  Avenir Health, William McGreevey from Georgetown University, Leo Epstein from Sciences Po, Paris ,  Lawrence Eleh  from  Université de Yaoundé II  and to participants in a seminar at the  Center for Global Development  for their contribution.
PESTEL Analysis Cyprus' hydrocarbon sector is currently under development and the situation is changing due to the detection of natural gas reserves. The following analysis includes issues and facts that either influence the sector or are inherent. The data were collected and gleaned from secondary data, as academic literature, government or international organizations websites, factsheets, reports, etc. Political issues influence Cyprus' hydrocarbon development since Cyprus joined the European Union in May 2004, and therefore is committed to European policies and legislative framework as well as to the Europe 2020 targets  [51]. Moreover, there is a particular situation known as the Cyprus Problem, Cyprus dispute or Cyprus conflict. After the military Turkish invasion in 1974, the country was divided into two main parts, the southern part which is under the control of the recognized Republic of Cyprus government, and the northern part which is under Turkish occupation  [52].
• Crowdsourced business intelligence (CrowdBI). CrowdBI is a new form of BI and it is presented in a data-driven manner. The data are collected from various crowdsourced data sources, including both online social media in the cyber space and pervasive sensing data in the physical space. All crowd-contributed business data are analyzed by a series of advanced machine learning  and data mining technologies to present valuable information about customers, products, brands, enterprises, and so on. The learned information can be further used to support a wide range of business decisions, such as product pricing, brand promotion, customer understanding, and product improvement. Unlike traditional BI that analyzes a small amount of data by existing analytical tools, CrowdBI requires advanced technologies to process heterogeneous crowdsourced data to extract diversified information about business.
C. A GENERIC FRAMEWORK We present a generic framework of CrowdBI as shown in Fig. 2 , which consists of four layers: data collection, data preparation, business knowledge learning, decision making and recommendation. • Data collection. We collect crowdsourced data for business intelligence analysis which contains offline data and online data. Generally, the crowdsourced data is obtained in two ways, including passive and active crowdsourcing. For example, most offline sensing data in the physical world (e. g. , mobile sensing data) is obtained by the active crowdsourcing, since users are recruited to collect spatio-temporal data via smartphones or other mobile devices. For online data in the virtual world (e. g. , data from social media), it is often acquired by passive crowdsourcing since users generate a lot of social data in daily life.
3) TRENDING ESTIMATION For business managers, one of their biggest common concerns is whether their business will thrive or fail in the future. Knowing about long-term trends, business managers can make decisions in advance. Lian et al. [39]  study the potential indicators for the long-term survival of a physical store. Specifically, they study four types of factors by using the data from Dianping and Yelp, including geography, user mobility, user rating, and review text. They find that the location and nearby places play an important role in the success of the shop, and usually less competitiveness and more heterogeneity is better. Based on the four types of factors, they use three prediction methods to estimate the trend, including Logistic regression, gradient boosted decision tree (GBDT), supported vector machine (SVM).
Kernel density estimation is then used to transform datasets of centrality and activities to one scale unit for analysis of correlation between them. Finally, they verify that retail stores and service activities in Bologna tend to concentrate in areas with better centralities  [64]. They further study the street centrality and their correlations with different types of economic activities by using the Multiple Centrality Assessment (MCA) model  [65]. They demonstrate that pure spatial organization can be indicative of the quality of retail and economic activities. Huff  [66]  reports that the probability that a consumer patronizes a certain shopping area is directly related to its size, and inversely related to its distance from the consumer. It further proposes a probabilistic model to determine the retail trade areas of the given shopping center.
The mobility features are formulated to measure the popularity of an area in terms of check-in patterns, including area popularity, transition density, incoming flow, and transition quality. Except for user mobility factors, user-generated reviews can also construct predictive features for analyzing users' preferences and assessing the attractiveness of candidate locations. In  [71] , three types of features are incorporated into a regression model to predict the number of check-ins at a candidate location, including review-based market attractiveness features, review-based market competitiveness features and geographic features. Lin et al. [72]  analyze the popularity of a business location using Facebook data in Singapore. They first extract a set of relevant features from several key factors, such as business categories, locations, and neighboring businesses.
1) WHY NOT VIDEOS? Traditional methods to analyze customer behaviors is based on video monitoring. These methods deploy plentiful cameras in stores and collect shopping data by recognizing customer activity from video, then they capture physical shopping behaviors through video analyses and image matching technology  [78]. However, there are at least the following four limitations for video-based methods. First, they demand good light and unobstructed line of sight to keep good accuracy. Second, image feature databases of customer behaviors are required for recognizing customer actions, which will take a high cost to gather before system deployment. Third, most of the analysis is done offline and need lots of computing resources. Even so, the accuracy of recognizing actions and target object is hardly satisfactory.
3) RISE AND FALL PATTERNS The rise and fall of business entities and their impact factors are also studied recently. Georgiev et al. [101]  provide an approach to modeling the impact of the Games on local retailers by analyzing a dataset mined a large location-based social service, Foursquare. The results suggest that the venue popularity rankings from subsequent time periods see their lowest agreement around the Olympic period and at places that are close to where the event itself and live broadcasts are held. Volatile Point-of-Interests (vPOIs) refer to those small businesses which appear and disappear quickly in cities. The prediction task for the rise and fall of vPOIs is valuable for both shopkeepers and administrators by supporting a variety of applications in urban economics. Lu et al.
4) MOBILE CROWDSOURCING PLATFORMS There are also many platforms that harness the power of citizens for business data collection, as shown in Table  6. Gigwalk 24 collects data of business performance in stores. It helps brands manage channel execution at scale to collect data and get execution issues fixed by the crowd of Gigwalkers. EasyShift 25 takes photos of products, check prices, and review promotions by thousands of smartphonearmed shoppers. The shoppers can check in-store displays, assess competitive dynamics (record how competitor brands are stocked and displayed), report product out-of-stock and pricing information, and monitor promotions and launches, which enables brands to greatly improve the in-store execution and the shopper experience. Twentify 26 enables companies to mobilize an on-demand workforce of smartphone users to collect data, and provides valuable insights and empowers better business decisions.
Specifically, they identify Airbnb's impact by exploiting significant spatiotemporal variation in the patterns of adoption across citylevel markets. By examining data from about 14,000 Airbnb listings and monthly revenue of approximately 3000 hotels in Texas, they find that the entry of Airbnb in Texas negatively affected hotel room revenue. Because of such negative externalities, the sharing economy and its regulation have become highly popular policy topics. Quattrone et al. [155]  propose to perform the socio-economic analysis of Airbnb adoption to envision regulations. After crawling Airbnb data for the entire city of London, they find out where and when Airbnb listings are offered. Then the socio-economic conditions of the areas are determined which benefit from the hospitality platform.
[157]  present the in-depth investigation on the Uber platform across locations in New York City and San Francisco. By analyzing the movements and actions of Uber drivers, they observe that surge prices have a small, positive effect on vehicle supply, and a large, negative impact on passenger demand. Chen and Sheldon  [158]  study how driver-partners on the Uber platform respond to surge pricing. They find that Uber partners drive more at high surge times, and that surge pricing significantly increases the supply of rides on the Uber system. This suggests that surge pricing significantly increases the number of trips that occur, and boosts the overall efficiency of the Uber system. Based on the vast amount of accurate data obtained by ridesharing services, Guo et al. [159]  analyze the nature of the demand and dynamic pricing mechanisms that match the supply with demand.