dataesr/scientific-paragraphs-categorization huggingface dataset:
When these heavy drinkers were re-interviewed, only 13% (41 of 321) still reported drinking three or more bottles per week, while 58% (185 of 321) reported drinking one bottle or less per week. Consequently, although their mean self-reported vodka consumption •1 (0•2) 1•3 (0•5) 5•2 (3•1) Mean (SD) vodka consumption reported at re-interview, in units of 200 mL alcohol per week 0•4 (0•7) 0•7 (1•1) 1•2 (1•9) Mean (SD) alcohol consumption reported at re-interview, in units of 200 mL alcohol per week 0•6 (0•9) 1•0 (1•1) 1•4 (2•1) Data are n (%). Groupings in this table are defi ned only by vodka consumption, but mean alcohol consumption includes all drinks, and is expressed in units of 200 mL of pure alcohol per week (the approximate alcohol content of one bottle of vodka). *6822 men were unintentionally interviewed twice, generally once during phase 1 and once during phase 2 of recruitment; this number excludes men with no follow-up at ages 35-74 years, or with evidence at baseline (ie, at the fi rst interview) of pre-existing disease (self-reported cancer, myocardial infarction, angina, heart failure, rheumatic heart disease, stroke, diabetes, tuberculosis, liver cirrhosis, or chronic hepatitis), or who at baseline had already quit drinking or smoking due to illness.
DedicationThis Article is dedicated to Gary Whitlock(1964-2013), who died after the fi nal revision. An appropriate epidemiological memorial is his excellent and enjoyable personal website of 18 200 mortality trends in many diff erent countries, http://www. mortality-trends. org/ (accessed Oct 18, 2013). DZ, RP, PBo, and PBr designed the study. DZ and AB coordinated its conduct. RK, AL, IK, VI, and TT coordinated the fi eldwork. AB, JB, SL, GS, PS, and XK prepared the fi nal database. SL, RP, PS, XK, and GW undertook the statistical analyses. SL, RP, DZ, and PBr drafted the paper, and all authors contributed to the fi nal version.
Acknowledgments This study is funded by  CTSU  core support from the  UK Medical Research Council, Cancer Research UK , and  British Heart Foundation , by  IARC  collaborative research agreements ( GEE/07/05  and  GEP/08/07 ), and by  EU  research grants ( ICA2-1999-10139  and  ICA2-CT-2001-10002 ). We thank the fi eldworkers for their help and the interviewees for their cooperation.
DedicationThis Article is dedicated to Gary Whitlock(1964-2013), who died after the fi nal revision. An appropriate epidemiological memorial is his excellent and enjoyable personal website of 18 200 mortality trends in many diff erent countries, http://www. mortality-trends. org/ (accessed Oct 18, 2013). DZ, RP, PBo, and PBr designed the study. DZ and AB coordinated its conduct. RK, AL, IK, VI, and TT coordinated the fi eldwork. AB, JB, SL, GS, PS, and XK prepared the fi nal database. SL, RP, PS, XK, and GW undertook the statistical analyses. SL, RP, DZ, and PBr drafted the paper, and all authors contributed to the fi nal version. Contributors.
MATERIALS AND METHODS We conducted a retrospective analysis of all cases referred from April 2010 to March 2014. Information relating to the cases was extracted from the database of the tele-expertise system. Ethics permission was not required, because patient consent to access the data had been obtained and the work was a retrospective chart review conducted by the organization's staff in accordance with its research policies. We also carried out a survey of all users in December 2013. The survey contained 50 questions. These were closed-ended, multiple-choice, and scale type questions, and open-ended questions. The questions were established after literature research combined with qualitative data collection (in-depth interview and participating observation). The survey was tested on three referrers and three specialists, in English and in French.
After the pilot testing, the survey was sent to all referrers and specialists registered in the database, regardless of whether or not they were active (i. e. , had logged in and sent or answered cases). Versions of the survey were made available in French and English. Web-based software (https: //www. surveymonkey. com/) was used for collecting the data. Data were examined with the usual methods for quantitative analysis, while the results of the open-ended questions were processed in a qualitative way. The present paper reports a preliminary analysis of the survey results.
ACKNOWLEDGMENTS We thank the volunteer case-coordinators and specialists, who represent the cornerstone of the system. We are particularly grateful to  Jean Rigal ,  Jean-Hervé Bradol ,  Marie Pierre Allié ,  Will Wu ,  Olivier Steichen ,  Christian Hervé ,  Ondine Ripka  and the  MSF legal department ,  Leslie Shanks ,  Katherine Gottwald ,  Raghu Venugopal ,  Pedro Pablo Pablo ,  Carme Baraldes ,  Eric Comte ,  Bertrand Draguez ,  Jean-Paul Jemmy ,  Saskia Spijker ,  Judith Herrera ,  Annette Heinzelmann ,  Micaela Serafini ,  Jarred Halton ,  Elisa Salvado , and all our field referrers.
Methods As the pharmaceutical industry is global, we have used for the analysis of the transactional activity of big pharmaceutical companies a comprehensive proprietary dataset that encompasses information about alliances, or formal contractual agreements, in the pharmaceutical industry worldwide. The database was compiled by querying specialized internet sites (leading sources for news releases and regulatory filings from companies throughout the world such as Business Wire and PRNewswire, as well as the companies' internet sites) for alliances made in this industry between 2001 and 2006. The data sample contains 3,909 companies and the number of deals is 8,160. For the analysis of the transactional activity of big venture capital firms, the data was drawn from VentureXpert, a comprehensive database of venture capital funds, portfolio companies, and deals owned and managed by Thomson Financial and the only database officially endorsed by the National Venture Capital Association.
Data from VentureXpert has been used extensively in studies of the venture capital industry. This paper examines the US VC market as it is by far the largest in terms of the amount of capital invested. The time periods examined here run from the first quarter of 1996 to the last quarter of 2008. During this time, 1,499 biotech companies received venture capital funding in the Figure  2  Relations between small innovative companies (SI) and their partners. Relations between small innovative (SI) companies and their partners, in particular, the large players in the industry and in the financial sphere (big pharmaceutical companies and large venture capital firms in the chosen example). Source: proprietary database of alliances for the pharmaceutical industry and VentureXpert database for the venture capital industry.
Visugraph software is used for network visualization. Nodes in the networks represent firms and links represent legal binding agreements between firms. USA in either one round or more and 1,158 firms provided venture capital in 5,456 deals. We define a syndicated deal or syndication as the collection of VCs that invest in a given portfolio company, i. e. , comprising those investing in a given portfolio company investment round as well as those investing in subsequent rounds. Many VC firms involved in syndications invest across a range of start-ups, creating a chain of investments in which the syndications form the links. Syndications thus define a network among VC firms, where the ties among them are their coinvestments  (Kogut et al. 2007). Empirical studies on business models are scarce and open innovation has essentially been analyzed in large multinational enterprises, drawing on case studies and interviews (e.
g. ,  Chesbrough 2003). One-dimensional summary statistics would filter out much of the interesting temporal and relational variation in the data  (Moody et al. 2005). A dynamic interfirm network perspective implies that we engage data on these firms in new ways. We used VisuGraph software which was developed to visualize relational change  (Gay and Loubier 2009). The recognition that visualization fosters theoretical insight for the study of social networks is recent, though visualization tools have always been the key elements in scientific advancement. As stated by  Moody et al. (2005, p. 11) , 'Dynamic maps provide a combined synthesis of information, allowing one to view the relevant abstract features of a given interaction system. ' VisuGraph software allows mapping of the temporal unfolding of firms' networked interactions and effectively displays their different network architectures as they vary through time.
Some deals are with major firms with high and continuous transacting activity (large histograms). Merck & Co. and Novartis interact otherwise essentially with many smaller companies that have very low and temporary transacting activity (very small histograms present most often at only one time period). Colored rectangles indicate the origin of the transacting partners (see insets). Though Merck & Co. makes numerous alliances with US companies and Novartis with Swiss companies, the many different colored rectangles beneath histograms indicate that transactions are global. The alliance database reveals that big pharmaceutical companies mostly leverage external assets (in-licensing; value capture or inbound innovation) between 2001 and 2006. Seventy percent to as much as ninety percent of the transactions of big pharmaceutical companies are in-licensing agreements.
Conversely, biotech companies are in effect on the sell side of the open business model (out-licensing; value creation and outbound innovation) and typically gear toward explicit, short-term milestones. A consequence of the constant search of new partners by dominant pharmaceutical companies is that it becomes extremely difficult for small companies to maintain themselves for an extended time in an industrial network. As illustrated in Figures  3  and 4 , most firms that interact with big pharmaceutical companies have in effect low and temporary transactional activity within the whole network. The database reveals that about two thirds of biotechs active in a given period do not make alliances in the next period. Most biotech firms are not active in more than two periods. Figures  5  and 6  illustrate how big VC firms operate for long time periods by looking at the behavior of two major VC firms.
(Bottom) The open business models of the major players in each business landscape are very different (period 2004 to 2005). Roche, for example (left graph; blue dot at the center of the graph), makes each year many new dyadic transactions with many different partners. Links represent mainly in-licensing activities. The partners which Roche interacts with both in 2004 and 2005 are few (inner circle). Conversely, for Alta Partners (right graph; blue dot at the center of the graph), novel partners are fewer than for Roche, and Alta Partners links them to partners with which it has recurring partnerships (inner circle). Source: proprietary database of alliances and VentureXpert database. Visugraph Software is used for network visualization.
DECENTRALISING THE CLOUD In early 2007, when Drizzle first sees the light, the industry of online data storage -a service allowing users to store, save and share data on one or several terminals connected to the internet -has "never felt better"  (Guerrini, 2010). Google, Amazon, Microsoft and Oracle, to name but a few, propose their storage platforms, each with its specificities and one common denominator: the 'cloud'. According to this model, the service provider is in charge of both the physical infrastructure and the software. Thus, the service provider hosts applications and data at once -in a location, and according to modalities, unknown or at best ambiguous to the user  (Mowbray, 2009). The so-called 'server farms' proliferate, to support and manage this increasing remoteness of data from users and users' terminals.
In return, users need to accept to 'pool' -put at the disposal of other users and their computersthe computational and material resources necessary for the operations related to the storage of content. As the service's terms of use point out: "The user acknowledges that Drizzle may use processor, bandwidth and hard disk (or other storage media) of his computer for the purpose of storing, encrypting, caching and serving data that has been stored in Drizzle by the user or any other users. The user can specify the extent to which local resources are used in the settings of the Drizzle client software. The amount of resources the user is allowed to use in Drizzle depends on the amount of local resources the user is contributing to Drizzle. " The interdependent and egalitarian model subtending the platform will allow its users to barter their local disk space with an equivalent space in the decentralised cloud, thereby improving the quality of this storage space, which will become permanently available and accessible.
PASSWORD AND USER RESPONSIBILITY In Dietrich's intentions, the role of the user-selected and user-generated password for the Drizzle system should have "stri[cken] the user as soon as he had access to the system for the very first time. " Indeed, the virtual form that is served to users upon subscription may come as a surprise: it informs that "We do not know your password as it never leaves your computer. Please, do not forget your password and use, if needed, your password hint. " The status of the password is thus negotiated, beyond its usual meaning of unique identifier vis-à-vis the system, to define, detail and legitimise the process of local encryption and decryption of data within the Drizzle system. This feature comes to symbolise the specificity of Drizzle's promise of security and privacy as well as users' trust, as it becomes the symbol and the graphical representation of the 'local' dimension of the encryption process -as it never leaves the computer of the user who created it.
The operations, for the most part automatically managed, that are linked to the protection of personal data are thus hosted on the terminals of users. Indeed, this entails a modification of the user's role within the service's architecture: node among equal nodes, it becomes a server itself, instead of a starting point and a final point for operations that are otherwise conducted on another machine or group of machines. Through the attribution of this status to the password, the developers of Drizzle are also proposing an alternative to the balance between the rights exerted by users on their own data and the rights acquired by the service provider on these same data -a balance that is usually heavily bent on the provider's side. However, this reconfiguration in the balance of rights comes with a trade-off.
DATA PRIVACY AND RESOURCE ALLOCATION Another aspect that contributes to define rights and responsibilities is the detailing of the conditions for allocation and management of the computational resources provided by the different computers participating in the system. As briefly described above, the choice to decentralise the platform makes it necessary, due to the very particular status of the resources used by the system, to detail several aspects in the terms of use: the role of computers belonging to users, the types of resources that Drizzle is able to use, their purpose. It also becomes necessary to detail the extent to which users are able to decideand communicate to their P2P client, thus to the system -the maximum quantity of local resources that the rest of the network/storage system can use.
This data includes their IP addresses, disposability and the amount of resources they are contributing (e. g. bandwidth, memory). […] Drizzle keeps track of how much storage space you have used and earned […] Drizzle collects statistical information for the purposes of monitoring, debugging and improving the system. This includes automatically generated problem, performance, network analysis and general usage reports, as well as logs of the connections and queries made to Drizzle's servers (including the involved IP addresses), as well as analytical data about the usage of the Drizzle website. However, none of this data contains information from your private or shared files. " Thus, the correct functioning of the allocation system indeed implies the gathering of several pieces of information concerning the material, computational and memory resources pooled by each participating computer.
A tool for regulation and promotion The CIVB (or Bordeaux Wine Council) was founded in 1948 and represents the three actors of the Bordeaux wine industry: wine growers, wine merchants and brokers. As such, the CIVB has three main missions. The first consists of marketing and promoting Bordeaux wines in France and abroad through advertising campaigns, digital communication, public relations and training. The second is dedicated to the acquisitions of economic data and management knowledge in relation to the production, marketing and sales of Bordeaux wines throughout the world. The third mission is technical and aims to improve the industry's understanding of various procedural issues concerning the quality of Bordeaux wines and anticipating new environmental and health requirements. Presidents of the CIVB are elected by the membership, chosen alternately by growers and merchants.
Data collection We drew on three sources to build the data set for the study. First, we collected perceptual measures of CS brand image, OEM-CS relationship quality, and OEMs' value perception with a survey of executives in OEMs conducted in 2007. We randomly sampled 2200 OEMs in manufacturing industries in Germany from the database of a commercial provider. To avoid sampling bias toward smaller OEMs as a result of their larger numbers, we imposed a quota for each firm size bracket (indicated by the number of employees) to be equally represented. We identified the heads of marketing and sales by telephone, which was successful in 989 cases. After sending an invitation and two reminders, we obtained 241 complete responses (response rate =24%). This response rate compares favorably with similar studies, which fall in the range of 15%-21% for surveys at the management level (e.
g. ,  Homburg, Droll, & Totzek, 2008; Reinartz, Krafft, & Hoyer, 2004 ). In the survey, we instructed informants to pick a typical component that the OEM sourced along with the brand name of a current CS for that component. Because research repeatedly suggests that the accuracy of survey data can be improved by obtaining responses from multiple informants  (Phillips, 1981; Van Bruggen, Lilien, & Kacker, 2002) , we collected data on OEMs' value perception and OEM-CS relationship quality from a second manager in the same OEM who was in charge of procurement for the component. 3 We received an additional 104 usable abbreviated questionnaires from these informants (response rate = 54%). We dropped four single-informant surveys from the analysis because of low informant confidence ratings. Next, we obtained archival financial statement information to match the CS brands provided by informants in the survey.
In the European Union, profit-and-loss statements are publicly available even for many private firms. We first identified the CS firm behind each CS brand name and then gathered archival financial information for those firms from Bureau Van Dijk's ORBIS database. In case of more complex ownership structures, we used financial results at the lowest level of aggregation available. Combining the two data sets yielded 101 observations for which financial measures could be retrieved from archives for at least four years between 2006 and 2010. In the remaining cases, either the CS firm could not be unambiguously identified from archives or financial measures for the CS had not been (fully) reported. For 42 of 101 observations, we use multiple-informant survey data; the remaining 59 are based on single informants.
We assessed non-response bias in two ways. First, a comparison of early and late responses to the survey revealed no systematic differences in the measures  (Armstrong & Overton, 1977). Second, we compared the demographic information from the commercial database and found that responding firms did not differ from the firms in the initial sample in terms of industry sector (p = 0. 77), annual revenues (p = 0. 40), and number of employees (p = 0. 99). Thus, we do not consider non-response bias a problem in this study. The availability of multiple responses from two informants per firm for 104 cases enabled us to assess the reliability of ratings provided by the managers surveyed. Following  James, Demaree, and Wolf (1984, 1993) , we calculated an index of inter-rater agreement to examine the degree to which informants from the same firm shared similar perceptions of a CS.
Thus, the four items adapted from extant research capture CS brands' credibility and their ability to signal quality  (Erdem & Swait, 1998; Erdem, Swait, & Valenzuela, 2006). Our measure of OEM-CS relationship quality comprises items for trust and commitment adapted from  Doney and Cannon (1997)  and  Morgan and Hunt (1994). 7 We measured the control variable OEM's value perception of CS with four items adapted from  Ulaga and Eggert (2006)  and  Menon, Homburg, and Beutin (2005). We calculated item scores for multiple responses available for the latter two constructs a Multiple replies permitted. 6 Because we conducted the survey in the fourth quarter of 2007, and given that many firms' 2007 fiscal years end earlier in the year, we used 2006 and 2008 data to compute profitability growth to ensure that measures were taken before and after the survey.
A doctoral student mapped the verbal ratings onto a seven-point scale. 9 We assessed reliability using a second rater who independently coded one-third of the sample. High intra-class correlation coefficients (ICC) indicate excellent consistency of the ratings (ICC = 0. 98/0. 78/ 0. 78; p b. 001) (Cicchetti, 1994). The ISBM B2B data resources library (http://isbm. smeal. psu. edu) describes Datamonitor as a "quick and reliable way to get data on key industrial sectors. " Datamonitor undertakes continuous quality control and cross-checking to ensure accuracy of the reports. The profiles draw from extensive primary and secondary research databases as well as complex modeling and forecasting tools to deliver reliable and consistent market analyses for a broad range of industries. We further tested internal consistency by extracting a measure of industry rivalry, which is inversely correlated with all three measures, providing support for their validity.
Altogether, these arguments reinforce our confidence in the validity and reliability of these measures. We operationalized technology intensity in the CS industry as the average ratio of R&D expenditures to revenues across the 100 largest firms within each industry. By using archival data of industry-level competitive environments, we avoid the potential subjectivity bias of surveybased assessments by managers who may not know the external environment well enough to make precise comparisons with other industries  (Homburg, Klarmann, Reimann, & Schilke, 2012). We include firm and industry-level control variables that could offer alternative explanations for the effects observed. Given that our rationale for the impact of CS brand image is based on power of CSs over OEMs, we include an archival measure of CS revenue to control for CS firm size.