dataesr/scientific-paragraphs-categorization huggingface dataset:
3738Å) and diamond anvil cells. The transmitting media was nitrogen. The pressure was determined using the shift of the fluorescence line of the ruby. The diffraction patterns were collected with a CCD camera, and the intensity vs. 2θ patterns were obtained using the fit2d software  [15]. A complete Rietveld refinement was done with the GSAS-EXPGUI package  [16]. The electronic properties for the different structures were analyzed within the Density Functional Theory (DFT) framework. We used a full-potential linearized augmented plane wave code, Wien2k  [17] , and GGA  [18]  to represent the exchange correlation potential. The positions for the "heavy" elements were taken directly from the experiments while fluorine coordinates were fixed at their high symmetry positions. We did not do any relaxation or structural minimization.
C. F. gratefully acknowledges support from the Brazilian agencies CAPES and Cnpq. This work was partially supported by the project TetraFer ANR-09-BLAN-0211 of the Agence Nationale de la Recherche of France. R. W. is fellow of CONICET-Argentina and gratefully acknowledges partial support from CONICET (Grant No. PIP 114-201101-00376) and ANPCyT (Grant No. PICT-2012-0609).
D. C. F. gratefully acknowledges support from the Brazilian agencies CAPES and Cnpq. This work was partially supported by the project TetraFer ANR-09-BLAN-0211 of the Agence Nationale de la Recherche of France. R. W. is fellow of CONICET-Argentina and gratefully acknowledges partial support from CONICET (Grant No. PIP 114-201101-00376) and ANPCyT (Grant No. PICT-2012-0609).
Specifically, project performance worsened in our sample (p < 0. 01) along with an increase in the dispersion of performance, as the relative overall compound (T*O) complexity increased, with better fit than T or O individually or additive T+O. In a further exploratory analysis, we examined the relationship between complexity and project size using a different data set of 30 major projects, all from one firm the oil & gas domain. This is of interest since it is common industry practice is to use cost (materiality) as a proxy for the degree of complexity or difficulty in assigning resources and in benchmarking performance. Observations of the expected capital cost of each project were available for each project in this dataset, but observations on performance were unavailable. In this iteration, we added another dimension of complexity -architectural ('A') -to denote the extent to which managerial choices in either the technical or institutional ('I', previously organizational 'O') !"#$%&#'()*!+)) ,-.
PRIMARY RESEARCH METHOD AND DATA Our main objective in this study is to identify the specific phenomena and features of projects that relate to our conceptualization of complexity and to test the relationship between them and project performance. Armed with the basic construct of the House of Project Complexity comprised of Technical, Institutional, and Architectural dimensions, we do this using a sample of LIPs that include much more detailed narratives than either of our two exploratory samples. Our approach uses coding of case-study write-ups to identify key concepts and relationships and further develop and refine the proposed framework. We proceed in three steps, beginning with formulation of a coding protocol, to relational analysis and framework development in the final step (Table  1 ).
We discuss the data, methodological details of each step, and the main outcomes of the analysis. While the earlier versions of the HoPC relied on data from the oil & gas sectors, we wanted to see if the HoPC construct was useful in other large project sectors, particularly large infrastructure projects. The data comprise in-depth case studies of 20 large projects. The case studies were prepared for the IMEC Research Program, a benchmarking study for best practices in large projects, based on interviews with key participants and questionnaires to project sponsors. The set of projects spans electric power, hydro development, roads, bridges, tunnels, urban transportation, and an airport. The earliest projects in the sample were undertaken in the early 1980s. Some were completed only recently in the 2000s.
In recent years, the Internet has given rise to many forms of user participation. These user contributions are usually voluntary, and result in the production of freely available public information goods (encyclopedias, video or photo databases, news, expertise, etc. ). Consumer rating and review systems such as Amazon's and TripAdvisor's are examples of participatory devices that have met increasing success over the past decade and are now extremely widespread. This way of assessing the quality of products takes various forms, depending on the design of the websites, but it has established a ! ! central and relatively standardized device: the so-called "online consumer review" (OCR), which comprises a rating and an associated written review. OCR systems allow any Internet users to offer their opinions on a broad range of products and services based on their personal experiences, and to thereby guide consumers' choices.
In other words, it is important to examine whether intensive contributors weigh as much in the mean scores of restaurants as the less active ones. To check this out, we split the contributors' database into five groups according to their reviewing activity (from 1 rating to 50 and more). We compared the groups in terms of their share in the total number of ratings, and their weight in the restaurants' average evaluation (each rating of a restaurant is divided by the number of evaluations the restaurant has received) (Table  9 ). For instance, in the case of Cityvox, the contributors that had posted only one rating produced 18% of the overall ratings and accounted for 21% in the final average evaluation of the restaurants.
And finally, as a user, either you know some other users that you trust, you'll read what they say as you would read a blog. Or you're searching and you are completely drowning in all that information. And finally, when you look at user behaviour, you realize that people end up looking at counters. They say "this restaurant, 500 reviews, a score of 4. 5 out of 5, it looks good to me. " And finally, the uploaded reviews, they are just useless. So yes, the goal was to index everything you can find, everything that is produced by Internet users and to find a way to deal with it in order to, on the one hand, describe a database correctly, with depth, so we can say "This address is a restaurant, it is a gourmet restaurant, the terrace is nice, reception is friendly, etc. "; and on the other hand, to rebuild the social graph of users connecting them to local places through shared affinities.
! The hypotheses are tested in a sample derived from the Panel Study of Entrepreneurial Dynamics II (PSED II) dataset. The distinctive feature of the PSED II is that it identifies and surveys nascent entrepreneurs (NEs) in the process of starting new ventures, thereby overcoming potential survivorship and recall biases typical of surveying entrepreneurs already in business  (Gartner, Shaver, Carter, & Reynolds, 2004). At the time of the research, data from the Timely Creation of New Organizations! initial data collection round of PSED II (A) and four follow-up waves (B-E) with the same NEs -each completed approximately one year apart -were available. Hence, data for our independent variables come from "Wave A" of PSED II data collection, which took place in September 2005 -February 2006, and data for the dependent variable comes from up to four years later.
The initial screening processes involved telephone interviews of 31,845 individuals, selected using random digit dial sampling procedures, throughout the USA. To be identified as a nascent entrepreneur during the screening process, the respondent had to exhibit the following characteristics: (1) they anticipated having some ownership in a new firm; (2) they had to have been actively trying to start a new firm in the previous 12 months; and (3) they could not have positive monthly cash flows covering all expenses and salaries for 6 of past 12 months. Of the NEs satisfying the screening criteria, further selection and volunteering criteria resulted in 1214 nascent entrepreneurs being subsequently interviewed by telephone in Wave A (Response rate 77%). The PSED II database is representative of the US adult population due to relatively high response rates and weights used to correct for differences in selection probabilities and non-response rates from random data collection.
! After the initial interviews, NEs in the PSED II sample were followed up at one-year intervals, and their progress was recorded. The PSED II database includes a time stamp (month and year, reported by the NE) for each activity that the NEs had completed following business opportunity recognition. Our primary dependent variable captures the length of time elapsed between (1) when an NE first came up with the business idea, and (2) when the same NE achieved two critical milestones of organizational formation: starting exchanges (i. e. making the first sale) and acquiring external resources (i. e. hiring employees or receiving external financing). These markers of organizational emergence echo prior research (e. g. Katz & Gartner, 1988; Reynolds & Miller, 1992; Tornikoski & Newbert, 2007). If the first sale took place at a different time (different month) than the first resource acquisition (hiring or funding), the time stamp of the activity that took place last was used to calculate the dependent variable.
! In an attempt to explain why some new organizations are established faster than others we adopted a perspective according to which a timely organizational emergence can be understood and predicted by viewing it as conditioned by the initial decisions of entrepreneurs regarding the opportunities they are to pursue. Using a large dataset of individuals who are in the process of building their ventures, and who have been followed up on an annual basis as they go through this process, we have found empirical evidence to conclude that initial opportunity characteristics exercise imprinting effects on the time it takes to create new organizations. More specifically, a technologically advanced business opportunity that is close to the knowledge domain of a nascent entrepreneur is likely to be able to move quickly towards organizational emergence.
! The dataset available to us (PSED II) presents some limitations that should be taken into account when evaluating these findings. In order to investigate variation in the time it takes to achieve certain milestones in the start-up process, we had to rely on data from those cases that actually reported having achieved these milestones during the follow-up period. Since the PSED data reflects average startup efforts in the population it contains a large amount of data from nascent entrepreneurs who set out to be self-employed without ever looking for external funding and with no intentions of hiring others  (Reynolds, 2007). Less than twenty per cent of nascent entrepreneurs achieve the milestones of first hiring or external funding within the four-year follow-up. Because of this feature of the dataset, as well as the overall attrition, the sample sizes available for our empirical models were limited (See Table  1  for the number of cases in each model).
! Regarding the operationalization of "novelty of technology" we used a simple dummy variable. We acknowledge that this operationalization is not optimal; technological novelty is far more nuanced than a simple yes / no answer. Unfortunately our empirical data is limited to what was available in the PSED II dataset. As such, we advise readers to accept our results guardedly, and, if possible, replicate them with alternative operationalizations. It should also be noted that our results are not representative of nascent entrepreneurs who do not achieve our selected milestones within the four-year follow up. We realize that a failure to achieve the milestones may be of the entrepreneurs' choosing (i. e. wanting to be self-employed and not looking for external funding) or due to their lack of success in achieving the milestones despite trying.
Qualitative information was also gathered on the organisation of marketing chains, as well as on the quality characteristics and demand at different stages. The quantitative data was gathered in the city's six wholesale (night) markets (spontaneous and planned). data search also included a sample of five retail markets representative of customer diversity. Because of the high number of vegetable types (more than 40) and their variability throughout the year, we focused on the types which accounted for 80% of transactions, which covered 8 to 16 vegetables depending on when the survey was made, with at least three ''regular'' vegetables (tomatoes, cabbage and water convolvulus). The surveys were made at times of supply variation: January, March, June and September. A representative sample of traders was interviewed by randomly walking through the markets and stopping to interview every fifth trader.
Data collection The database of the Portuguese government's export promotion agency randomly generated a sample of 2,352 firms, with named individuals responsible for foreign operations. Of these, 519 usable questionnaires were returned (22% response rate). To increase variance and generalizability of the results, we considered exporters in multiple industries and regions. Respondents held positions such as president, marketing director, managing director, and exporting director. Descriptive statistics and correlations are included in Table  5. To evaluate possible non-response bias, we categorized the valid questionnaires into early and late responses  (Armstrong and Overton, 1977). As explained by  Armstrong and Overton (1977) , those who respond later in a survey do so because of the increased stimulus and are thus expected to be similar to non-respondents.
The checks assessed the presence of NCD risk factors and the concurrent prevalence of DM and hypertension. Previous published reports such as those by Puaikura 7 and the Tutakimoa Lifewise Project 8 have analysed 'wellness checks' data to describe the prevalence of risk factors within the community. However, to date, none of the information on risk factors captured in the workplace has been formally published. This muchneeded information will provide an important first step towards developing an evidence-based policy framework for evaluating and improving the effectiveness of workplace interventions to promote healthy lifestyles and reduce the risk of NCDs in the Cook Islands. The aim of the present study was to determine the sociodemographic characteristics, the presence of selected NCD risk factors and the prevalence of DM and hypertension among all government workers on Rarotonga who took part in 'wellness checks' in 2012.
A single resting BP measurement was taken, unless it was raised, in which case a second reading was taken. MoH staff were trained to distribute the questionnaires and gather anthropometric and other study data. Information was recorded onto the MoH Community Health Assessment Form, and the data were entered into the electronic patient information system (Medtech, Auckland, New Zealand). Participants who screened positive for raised blood glucose or blood pressure were referred to the general health care system for diagnosis, treatment and care.
Data variables, operational definitions and data collection Data variables included age; sex; BMI; reported history of DM or hypertension; number of days each week and number of servings per day of fruit and vegetables consumed; intensity, number of days per week and time in minutes of physical activity (intensity of physical activity was classified subjectively as light, moderate or vigorous); random and fasting blood glucose; and blood pressure, measured in the sitting position. Operational definitions were as follows: high level of fruit and vegetable consumption = five servings of fruit and vegetables/day 7 days/week; any number of servings of fruit and vegetables less than this was defined as low level of consumption; low level of physical activity = engaging in 150 min of moderate physical activity per week, defined as leisure time activities, gardening and housework, and excluding activity done as part of paid employment; obesity = BMI 30 kg/m 2 ; hypertension = history of hypertension or resting sitting systolic pressure 140 mmHg and/or diastolic pressure 90 mmHg.
Sample The empirical study is on companies listed on the SBF 250 Index of the 250 largest French companies listed on the Paris Stock Market between 2004 and 2006. The data on ownership and governance characteristics were collected manually from annual reports and reference documents. Accounting and financial information are available in the Worldscope database. We excluded foreign companies that were not subject to joint auditing  (11) , banking institutions, insurance companies and real-estate companies given their specific accounting presentation and audit regulations  (33) , as well as companies for which certain data were unavailable (76). This selection procedure allowed us to have a sample of 130 companies over three years, i. e. , 390 observations.
Following discussions, we presented a formal research proposal to the management to finalize the purpose and scope of the research. This process provided open access to data and the people who participated in the projects. The first project was ongoing, while the other two projects were already completed. The completed projects involved the management of energy within semi-conductor chips, resulting in multiple applications for the ongoing project. At this time, development was still in the FFE stage. We selected completed over uncompleted projects to account for the ex-post rationalizations resulting from the interviews on completion of the development. The second project was selected because it resulted in a successful launch in the market. The project comprised the development of secured memory in telephones and electronic accessories.
Acknowledgments The author would like to thank  ST Microelectronic  and the staff from Tours and  Rousset, France , for having supported this research. The easy access to managers, their open-minded approach for making data accessible and their support all along the interviews contributed to a great extent to the quality and interests of the results. The author would like to thank his colleagues,  Prof R. Soparnot  and  Prof A. Rychalsky , from  ESCEM , who contributed to this research by supporting the interviews process.
MATERIALS AND METHODS We conducted a retrospective analysis of all dermatological cases referred by MSF field doctors to the MSF telemedicine platform from April 2010 to February 2014. All cases classified by the IT system as dermatological were retrieved automatically (group 1). To be exhaustive, a manual check of the database was then performed by identifying the most active expert profiles (using the function "user case history") and all potential cross specialties such as internal medicine, infectious diseases, pediatric, and ear nose throat (ENT) in order to extract all dermatological cases (group 2).
WORKLOAD We performed a descriptive analysis involving the assessment of the cases submitted. First, we extracted data about system performance, as follows: the number and language of cases, the median delay in providing the first specialist response, and the median delay in allocating a new case. Then, we extracted information on case characteristics, including the countries of origin of the experts and their specialties, the most common topics, the age of the patients, the number of images per case, and the number of follow-up reports and cases of poor quality information reported by specialists.
SURVEY In May 2014, we carried out a short-anonymous survey of all users involved in the dermatology cases. The survey design was based on a previous, larger survey, with 50 questions, established after a literature search combined with qualitative data collection. This survey showed that telemedicine was helpful and improved the management of the patient. We wanted to focus some of these questions on dermatological topics. We focused on (i) follow-up because in this large survey it was one of the main lessons learnt and (ii) quality of the referral because the quality of the expertise depends on the quality of the information send  (14). The survey had six questions for the referrers and seven questions for the specialists (closed-ended, opened-ended, scale type, and multiple choice questions).
Data collection The data collected included sociodemographic information such as age, sex, educational level, and marital status, as well as clinical characteristics such as diagnosis according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, severity of patient's illness at the time of discharge (Clinical Global Impression-Severity scale [CGI-S]), and how much the patient's illness improved since his or her entrance to the hospital (CGI-Improvement scale [CGI-I]). 35 These two scales range from 1 (normal or very much improved) to 7 (among the most severely ill patients or very much worse). The study also considered the functioning of patients at the time of discharge (Global Assessment Functioning [GAF]). 36 The GAF assigns a clinical judgment to the individual's overall functioning level (ie, psychological, social, and occupational/school functioning) and ranges from 0 (inadequate information) to 100 (superior functioning).
The variables relevant to the models were selected from the bivariate SATISPSY-22 index analysis, based on a threshold P-value 0. 20. An additional variable was included in the models because of its sociodemographic interest (age). The final models incorporated the standardized β coefficients, which represent a change in the standard deviation of the dependent variable (satisfaction) resulting from a onestandard-deviation change in the various independent variables. The independent variables with the higher standardized beta coefficients are those with a greater relative effect on satisfaction. All of the tests were two-sided. Statistical significance was defined as P0. 05. The SPSS version 18. 0 software package (SPSS Inc. , Chicago, IL, USA) was used to perform the statistical analyses.
Acknowledgments The authors are grateful to all of the patients for their participation in the study. This work was supported by institutional grants from the 2009  Appel Offre-Recherche Clinique Assistance Publique, Hôpitaux de Marseille. The sponsor of this research was  Assistance Publique ,  Hôpitaux de Marseille ,  France , and its role was to control the appropriateness of ethical and legal considerations.
Results We obtained baseline interview data from 210 002 adults in 73 060 households. After exclusion of those too old or young to have any follow-up at ages 35-74 years, those with previous illness, and those who had stopped smoking or drinking because of illness (appendix pp 7-8), 151 811 remained for prospective follow-up of mortality. Table  1  (also appendix p 9) shows the participants' characteristics, subdivided by their self-reported vodka consumption. Relatively few (compared with controls in our retrospective study)  10  reported drinking one or more bottles per week of vodka: 19% (15 347 of 79 311) of the men and 2% (1209 of 72 500) of the women. Among them, intake of alcohol from other sources was only about a tenth that from vodka. Since intake of alcohol from other sources was much less than (and correlated with) intake from vodka, its consequences could not be studied.