dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis The associations between (i) the presence/absence of COI and study outcome, (ii) the presence/absence of COI and the topic of the article, and (iii) the topic of the article and its outcome, were assessed in Fisher's exact tests, with the function fisher. test of R  [5]. For a given article, the effect of its topic (i. e. efficacy or durability) on its number of citations (TC), as recorded on September 11, 2015 in the WoS, was evaluated with a mixed linear model, using the lmer function of R. The data were normalized by log(TC+1) transformation. The number of authors was included as a fixed effect and the identity of the leading author of each article was treated as a random effect. The age of the paper (i. e. 2015 minus the year of publication) was also considered as a fixed effect.
Policy implications Scientific journals should make it obligatory for authors to publish a COI statement, as such statements remain too rare (Fig  2A ). However, COI statements are not a panacea and do not absolve scientists and industrial partners of their obligation to deal with COIs  [14]. It is almost impossible to avoid non-financial COIs, but it does seem to be possible to limit the occurrence of professional and funding COIs in studies investigating the performance and impact of GM crops. However, it is desirable for these studies to be at least partly funded by GM crop companies, because these companies are the principal beneficiaries of these products and should take responsibility for the impact of their products on the environment and health. One way of combining the possibility of GM crop company funding with an absence of professional and funding COIs would be a system of indirect financial support for research from the industry: GM crop companies and other stakeholders (governments, NGOs) would make a financial contribution to a common pot managed by an independent agency.
Acknowledgments Data are available in S1 Table. We would like to thank  C. Brouat ,  V. Calcagno ,  T. Candresse ,  B. Facon ,  X. Fauvergue , and  T. W. Sappington  for their comments and insights on drafts of this manuscript and two reviewers for their valuable comments on a previous version of this manuscript. We would like to thank  Sheldon Krimsky  and  David Resnik  for valuable comments on an earlier version of the article.
The data sample The data used in this research is a private data provided by a top-tier US actuary company. It includes extensive information on stock options exercises and cancellations for 12 US firms listed in the NYSE and the NASDAQ (6 of them are large caps). One of these firms was acquired in 2006, and consequently unlisted since then (see company F7 in table-1). As reported in table-1, the data set is about 542 stock option plans granted over the period from 1983 to 2006 to 7,125 employees 8  located in 16 different countries. However, it is important to underline that 98% of the stock option holders are based in the USA. The plans expiration periods range from 5 to 10. 5 years. More specifically, the database includes details on individual grants characteristics, namely the employee ID, the plan ID, the number of granted options, the option strike price, the vesting schedule and the expiry date.
It also comprises the following items regarding 35,086 individual exercise transactions related to these grants: the employee ID, the plan ID, the exercise date, the exercise stock price and the number of exercised options. In addition, the database contains individual information on options forfeitures, mainly including the forfeitures dates, the number of options forfeited and the related plan ID. In order to cover all the events of zero-payouts and cancellations, I identified all the plans that expired worthless (i. e. resulting in zero payout at maturity) using share price data from Bloomberg proprietary database. I then determined for each employee the number of outstanding options that ended in a zero payoff by crossing their granted plans IDs with the IDs of the plans identified in the previous step.
I obtained a total number of 49,210 individual cancellation/expiry cases. The breakdown of this figure with respect to each firm is reported in table-1. Recall that the focus of this research is the exercise patterns of standard employee stock options. Therefore, I dismissed from the dataset all the items related to specific grants whose vesting is contingent upon performance conditions. Actually, the initial dataset included 1,251 exercise transactions related to 7 performance stock option plans. All these specific plans were granted to top executives and senior managers. The 542 plans comprised in the scope of this study are Bermudianstyle stock option contracts with vesting schedules subject to service conditions. Some contracts are immediately vested (i. e. do not include vesting periods).
The other contracts include either a cliff vesting or a gradual vesting. The vesting periods range from 2 to 9. 5 years. All the contracts were granted almost at-the-money and do not comprise buyback provisions. Moreover, the options are not transferable and the beneficiaries are precluded, as insiders, from short selling the firm's stock according to section 16-c of the Securities Exchange Act. This prohibition stands for a hedging restriction. As pointed out earlier, one of the strengths of this research is the quality of the dataset used. As far as I know, this research is one of the most comprehensive empirical studies on employee stock option exercise patterns in the American context.
Construction of the variables The parameterization of the CPT model relies on two types of input variables. The first set of variables characterizes the exercise policy of the employee. The second set is specific to the economy of the stock option contracts. Bermudian-style options) that do not include performance-related vesting provisions. Their vesting is either immediate or achieved after specified periods varying from 2 to 9. 5 years. The options maturities are from 5 to 10. 5 years. The first set of variables comprises the exercise stock price ratio and the cancellation rate. These variables are directly computed from the data sample described earlier. In particular, the cancellation rates were determined at the individual level (i. e. for each holder and for each plan), on an annual basis, as the number of the yearly forfeited and out-of-the money expired options divided by the total number of options granted with respect to a given plan.
The final stock price levels were assimilated to the split-adjusted closing stock price at the expiration date normalized by the strike price. These ratios were computed using the data from the Bloomberg database. Note that for the specific case of the outstanding plans (representing roughly 11,750 exercise transactions), I used the closing share prices on October 29 th 2010 as proxies for the final stock prices, except from the firm F7 for which the closing stock price as of July 6 th 2006 was used instead. In addition, I must emphasize that, opposite to  Carpenter (1998)  conducts to introducing a bias in the variables estimates due to overweighting some exercise behaviors (i. e. employees with the largest stock option holdings) against others. The second type of variables is mainly constructed based on financial data from the Bloomberg proprietary database, except from variables proper to the stock option contract design, namely the maturity dates and the vesting periods.
These were drawn from the exercises data sample. Specifically, I used as a proxy of the risk free interest rate the prevailing US T-bills rates at the inception of each plan. Similar to Carpenter (1998), I used an equity risk premium of 7%, which is roughly the arithmetic average premium over the period from 1958 to 2005 reported in Ibbotson Associates  (2006). I computed the expected stock price return using the CAPM: the risk free rate plus the risk premium times the beta of the company at the grant date. The latter is computed against the relevant benchmark 9  using daily returns over the 1-year period prior to the grant date. In addition, dividend yields were estimated using recorded dividend data over the year prior to the exercise date. In the same manner, stock return volatilities were computed over the 360-day period prior to the exercise date.
Table-2: Variables summary statistics This table reports summary statistics on the variables used in this empirical study. These include two types of variables: variables describing the exercise patterns and variables characterising the economy of the stock option contracts. The option exercise variables are constructed from a data sample of option exercises at 12 US companies. This set of variables includes the exercise stock price ratio " S τ ", the lifetime of the option " τ ", the cancellation rate "η" and the final stock price ratio " T S * ". The variables specific to the economy of the stock option contract include the maturity of the option "T", the vesting period "tv", the stock dividend yield "q", the stock price returns volatility "σ", the stock price Beta "β", the stock price expected return "µ" and the risk free rate "r".
In other words, structural change can only be witnessed in the medium or the long term. Which is why longer the time frame, the more meaningful the comparison is expected to be. Our dataset compares years 1997 and 2011, rather than 1995 and 2013 because of the non-availability of data on certain industries in years 1995-1996. This is clear from Fig. 4. Also, the year 2012 witnessed the transition from the 3-digit industry classification to 4-digit classification, resulting in some significant outliers. Hence, 1997 and 2011 will serve as our years of comparison in this study. Figure  4  plots the available data on all 2-digit level manufacturing industries, 1995-2011. Year 1995 and 1996 have been dropped from the dataset because of the non-availability of data on industries 15-30 as per the ISIC 2-digit classification.
These 50 municipalities are then matched with their 2011 entries-only retaining data on these 50 municipalities in order to study the complexity dynamics in the same municipalities over time. Accordingly, we obtain a matrix of 1100 observations for 2011 as well. Aggregation by industry. Since the original data is disaggregated at the 4-digit industry code-which is required for calculating the RECI-we aggregate the variables "output", "wages", "employment" and "number of firms" up to the 2 digit classification of industries which is then used for calculating our "Competition" and "Diversity" indexes. As a result, we obtain the values of these variables per municipality per 2-digit industry. This municipalityindustry data is subsequently used to test the hypothesis: Are inter or intra industrial externalities (from the available "urban variety" and local competition during the base year) significant predictors of change in complexity over time? Since our data is at the lowest available geographical level of disaggregation (municipalities), it arguably lends a very close look at the dynamics occurring at the regional level.
This ensures that the analysis is at the same level-municipality-as the other variables in our regression equation 12. One of the benefits of using this index is that it is calculated in relation to the national (sample) average; in other words, it normalizes local competition with the national average. This "correction" is extremely useful when applied to a developing country manufacturing dataset because many industries are still in their infant stage 13. Competition hence, represents number of firms per worker per industry per municipality compared to the sample average (50 biggest municipalities). Average Competition represents the number of firms per worker (across all industries) in the municipality in question, compared with the sample average. Accordingly, greater average competition represents more firms per worker in a municipality and vice versa.
This measure ranges between 0. 04 and 9. 83 in our dataset. Diversity. The current study devises a new variable of "diversity in production" for each municipality (not to be confused with the diversity statistic that goes in the construction of RECI). Just like the RECI and Average Competition, "Diversity" is calculated at the level of municipality. We calculate this variable is by taking the ratio of the output of a single industry in a municipality relative to the total output of that municipality across all industries, thus arriving at a vector of relative shares of all industries in a municipality. Since we do not have any information about the distribution of such a vector, we simply take the standard deviation of this distribution. The result is a unique value for each municipality that we use as a measure of the "diversity" of a municipality.
Hence, services are not included in this study. An interesting topic for future research can be to study whether knowledge externalities across services sector (or the whole array of economic activities) in Colombia lead to greater economic complexity over time. 9 Since we are interested in looking at inter-industry/intra-industry knowledge externalities as a predictor of growth in economic complexity, we aggregate the variables to arrive at 2-digit industry level classification even though the dataset details ISIC values at the 4-digit level of classification for each industry in each municipality which is too disaggregated for the purposes of this study. 10 Three outliers have been removed to facilitate the visualization. 11 Note that  Hausmann et al. (2014)  define a parallel measure for products -called the Product Complexity Index (PCI) -by calculating the average diversity of the regions that make those products and the average ubiquity of the other products that these regions make.
provides a comparative picture of Colombia's exports in years 1997 and 2011 respectively. Panel A depicts the exports data as a Tree-map while Panel B is a Product Space visualization. The trend of increasing specialization or reduced diversification of Colombia's export basket is clearly evident, even though the volume of trade grew five-fold over this period. Source: Hausmann et al. (2014), reproduced from the Atlas of Economic Complexity. This figure is covered by a Creative Commons Attribution-ShareAlike 3. 0 Unported License. Data Construction of the data set Years of observation. The panel dataset studied is derived from the Annual Manufacturing Survey or Encuesta Anual Manufacturera 7 of Colombia on output, employment, wages and number of firms per municipality per ISIC 4-digit industry classification (revision 3) for all years from 1995-2013.
Our aim was to review the studies which assessed potential surrogate endpoints for overall survival (OS) in breast cancer trials. A Literature search in PubMed database of studies which assessed potential surrogate endpoints for OS in breast cancer trials was conducted. The surrogacy was assessed with the German institute of Quality and efficiency in Health Care's (IWQiG) framework and the Fleming hierarchy. Thirteen studies were identified. At the neoadjuvant setting, two individual patient data (IPD) meta-analyses and one aggregate data meta-analysis assessing surrogacy of pathological complete response (PCR) were identified. Trial-level association was calculated in one study and the squared correlation was 0. 24. Therefore PCR was not judged to be valid surrogate for OS at the neoadjuvant setting according to the IWQiG framework and Fleming hierarchy.
Search strategy and selection for studies A search of literature published in English in PubMed database (from 1966 to March 2015) was conducted. The following strategies were used in the search: ("surrogate" [All Fields] AND "endpoints" [All Fields]) OR "surrogate endpoints" [All Fields] AND ("neoplasms" [MeSH Terms] OR "neoplasms" [All Fields] OR "cancer" [All Fields]). References of articles were also examined.
This issue has been addressed in works dealing with weakly serial dependence, see, e. g. Hsing [1991]  and  Drees [2000]. The main message from these studies is that usual EVT methods are still valid, only the asymptotic variance of estimators may differ from that in the i. i. d. case. Although the selection of the cutoff point and the serial dependence in data have been handled separately, the literature addressing these two issues are mutually exclusive. In the bias correction literature, it is always assumed that the observations form an i. i. d. sample; in the literature on dealing with serial dependence, the choice of k is assumed to be sufficiently low such that there is no asymptotic bias. Therefore, it is still an open question whether we can apply the bias correction technique to datasets that exhibit weakly serial dependence.
3. We remark that the heavy-tailed feature of the GARCH(1,1) model does not depend on whether the innovations follow a heavy-tailed distribution. Nevertheless, empirical evidence supports using heavy-tailed innovations for modeling financial time series, see, e. g. McNeil and Frey [2000]  and  Sun and Zhou [2013]. Correspondingly, we use the Student-t distribution as the distribution of innovations. 2 All parameters in the simulated GARCH(1,1) model are equal to the estimates from the real data application in Section 7. (Model 4) GARCH(1,1): X t given as in Subsection 5. 3 with λ 0 = 8. 26 × 10 -7 , λ 1 = 0. 052, λ 2 = 0. 941. The innovation term follows the standardized Student-t distribution with degree of freedom ν = 5. 64. Following  Kesten [1973]  we calculate the extreme value index γ of the series in Model 4 at 0.
Results Regarding the estimation of the extreme value index, we observe that even with a rather high level of k, our asymptotically unbiased estimator does not suffer from a significant bias, at least for the first three models; see Figure  2 3 4  Regarding the estimation of high quantile, we observe from Figure  6 -9 that our goal in reducing the bias is well illustrated on finite sample when using large k values. In addition, the RMSE of our asymptotically unbiased quantile estimator stays at a lower level than that of the original Weissman estimator for high levels of k. It is remarkable that the reduction in RMSE is higher for dependent series than for independent series. To conclude, the simulation studies show that under bias correction, the estimators for extreme value index and high quantile remain stable for a wider range of k values even if the dataset exhibit serial dependence.
Application We apply the asymptotically unbiased estimators on the extreme value index and high quantiles to evaluate the downside tail risk in the Dow Jones Industrial Average (DJIA) index. We collect the            daily index from 1980 to 2010 and compute the daily loss returns. The indices and loss returns are presented in Figure  10a  and 10b. From the figures, we observe that although the loss return series can be regarded as stationary, there is evidence of serial dependence such as volatility clustering. More concretely, by fitting the GARCH(1,1) model with Student-t distributed innovations to our dataset, we obtain estimates as λ0 = 8. 26 × 10 -7 , λ1 = 0. 052, λ2 = 0. 941 and ν = 5. 64. The existence of serial dependence prevents us from treating the series as i. i. d. observations.
DATA AND SAMPLE SELECTION To investigate if the availability of insurance on a company's debt may affect the debt restructuring process, we first need to identify a full set of in-and out-of-court renegotiations undertaken by distressed firms. Our initial sample includes all bankruptcy filings (under either Chapter 7 or Chapter 11) and distressed exchanges completed by U. S. rated companies over the period January 2007-June 2011, as reported by Moody's Default and Recovery Database. Bankruptcy filings include prepackaged Chapter 11 cases, which constitute regular credit events in CDS contracts. According to  Moody's (2000) , a distressed exchange is a debt restructuring where (i) the issuer offers creditors a new or restructured debt, or a new package of securities, cash or assets, that amount to a diminished financial obligation relative to the original obligation and (ii) the exchange has the effect of allowing the issuer to avoid a bankruptcy or payment default.
The frequency of bankruptcies among reference entities with very liquid CDSs is even lower (52%), partly due to the fact that around 85% of the defaults of these firms occur in 2009-10 (when distressed exchanges are relatively more frequent), against a corresponding percentage of 60% for reference entities with less liquid CDSs. Throughout our investigation, we focus on distressed exchanges and bankruptcies, while dismissing the default events due to missed payments that sometimes precede debt renegotiations. After the expiry of a grace period, missed payments of interests or principal are considered default events by Moody's and reported in the Default and Recovery Database. A missed payment also produces a credit event ("failure to pay") under CDS specifications and may therefore have an impact on our analysis.
TRADITIONAL DETERMINANTS OF DEBT RESTRUCTURING In assessing whether distressed reference entities are more prone to bankruptcy than other firms, we need to control for a set of variables that former literature has found to be significant determinants of the restructuring choice, that is, operating performance, leverage, and debt structure. 7  Data on those variables at the fiscal year-end prior to default are collected manually from the 10-K filings available from the EDGAR database. Specifically, data on operating income and leverage are obtained from the consolidated financial statements, while the debt structure is detailed in the notes to the financial statements.
For this purpose, we consider both the debt issues that were originally registered with the SEC, and those issued under Rule 144A and subsequently registered with the SEC. Data on the securities registrations are available from the SEC's EDGAR database. We also record, for each company in the sample, the number of public issues outstanding at the time of default, as a further proxy of potential creditors' coordination problems. As a final measure of the overall complexity of the debt structure, we include firm's size, computed as the natural logarithm of total assets. Large firms are likely to experience more severe creditors' coordination problems. However, very large companies can benefit from the too big to fail effect in times of crisis.
Information on the financial covenants imposed on bank debt and bond issues is obtained from Dealscan and Mergent Fixed Income Securities Database (FISD), respectively. We do not find significant differences, either in mean or in median, in the covenant structure of firms with a different restructuring outcome. Furthermore, we do not find the number of financial covenants to vary significantly between reference entities and other firms. The results are available from the authors upon request. In Panels B and C of Table  2 , we replicate the comparison between restructuring methods for both CDS reference entities and nonreference entities. The purpose is to highlight differences in the two groups that could help to shed some light onto any peculiarities that may characterize the restructuring choice of reference entities.