dataesr/scientific-paragraphs-categorization huggingface dataset:
gov but for only 62% in the published article. A study published in 2009 found that 73% of articles published in journals with a high impact factor reported serious adverse events  [33]. Nevertheless, a more recent study showed that only 34% of reviewers examined information registered in a trial registry  [34]. For developers of reporting guidelines, such as the CONSORT group, and for editors, our study questions the current way of reporting trials and the peer-review process. In ClinicalTrials. gov, results are posted in a standard tabular format without discussions or conclusions. Using templates with mandatory reporting of some  elements may facilitate the work of researchers by reminding them what they need to report and by standardizing their reporting. Including a template for reporting the results as part of the CONSORT guidelines could be useful to improve the completeness of trials' publication.
Furthermore, after the data results are submitted, ClinicalTrials. gov staff members review the submissions before public posting  [18]. Data providers may be asked to clarify items or make corrections. This systematic verification could also contribute to the completeness of data posted. These results may help convince publishers of the value of changes in the presentation of the results section of articles (standardized tabular format rather than narrative text) or of implementation of reporting guidelines. To improve the quality of reports of clinical trials, journals-even those endorsing the CONSORT statement-must move from their current position of passive endorsement (for the vast majority of them) to a more active implementation of CONSORT guidelines  [35, 36]. Although the reporting of results was more complete at ClinicalTrials.
gov than in the published articles, reporting at ClinicalTrials. gov is still suboptimal and could be further improved. Some elements, such as the number of patients assessed for eligibility, are nearly never reported at ClinicalTrials. gov. Other elements can be improved upon, such as the reporting of results for binary data. At ClinicalTrials. gov, the percentage of events rather than the number of events is frequently reported. Both the number of events and the number of patients analyzed per arm are needed to perform meta-analyses of binary data. Other elements raise some important issues, including the frequent reporting of nonserious adverse events observed in more than 5% of patients, 5% being the default frequency threshold for reporting nonserious adverse events at ClinicalTrials.
When assessing the completeness of reporting of efficacy results, we focused on a single primary outcome. If several primary outcomes were registered at ClinicalTrials. gov, we assessed the completeness of reporting for only the first primary outcome registered. Data extraction was not blinded to the source of data (ClinicalTrials. gov or published article) because blinding would have been impossible to achieve. Completeness of reporting was assessed as a binary outcome (all elements reported versus not all elements reported), so other assessment approaches may result in different findings. Finally, we could not determine whether publication, time to publication, and completeness were associated with risk of bias in trial design or conduct because ClinicalTrials. gov contained insufficient methodological information for assessing risk of bias  [21, 38].
The FDAAA requires that results from clinical trials of FDA-approved drugs and devices conducted in the United States be made publicly available at ClinicalTrials. gov within one year of trial completion. ClinicalTrials. gov-a web-based registry that includes US and international clinical trials-was established in 2000 in response to the 1997 FDA Modernization Act, which required mandatory registration of trial titles and designs and of the conditions and interventions under study. The FDAAA expanded these mandatory requirements by requiring researchers studying FDA-approved drugs and devices to report additional information such as the baseline characteristics of the participants in each arm of the trial and the results of primary and secondary outcome measures (the effects of the intervention on predefined clinical measurements) and their statistical significance (an indication of whether differences in outcomes might have happened by chance).
Researchers of other trials registered in ClinicalTrials. gov are welcome to post trial results as well. Here, the researchers compare the timing and completeness (i. e. , whether all relevant information was fully reported) of results of drug trials posted at ClinicalTrials. gov with those published in medical journals. What Did the Researchers Do and Find? The researchers searched ClinicalTrials. gov for reports of completed phase III and IV (late-stage) RCTs of drugs with posted results. For a random sample of 600 eligible trials, they searched PubMed (a database of biomedical publications) for corresponding publications. Only 50% of trials with results posted at ClinicalTrials. gov had a matching published article. For 202 trials with both posted and published results, the researchers compared the timing and completeness of the results posted at ClinicalTrials.
gov and of results reported in the corresponding journal publication. The median time between the study completion date and the first results being publicly posted at ClinicalTrials. gov was 19 months, whereas the time between completion and publication in a journal was 21 months. The flow of participants through trials was completely reported in 64% of the ClinicalTrials. gov postings but in only 48% of the corresponding publications. Results for the primary outcome measure were completely reported in 79% and 69% of the ClinicalTrials. gov postings and corresponding publications, respectively. Finally, adverse events were completely reported in 73% of the ClinicalTrials. gov postings but in only 45% of the corresponding publications, and serious adverse events were reported in 99% and 63% of the ClinicalTrials.
gov postings and corresponding publications, respectively. What Do These Findings Mean? These findings suggest that the reporting of trial results is significantly more complete at ClinicalTrials. gov than in published journal articles reporting the main trial results. Certain aspects of this study may affect the accuracy of this conclusion. For example, the researchers compared the results posted at ClinicalTrials. gov only with the results in the publication that described the primary outcome of each trial, even though some trials had multiple publications. Importantly, these findings suggest that, to enable patients and physicians to make informed treatment decisions, experts undertaking assessments of drugs should consider seeking efficacy and safety data posted at ClinicalTrials. gov, both for trials whose results are not published yet and for trials whose results are published.
ClinicalTrials. gov provides information about the US National Institutes of Health clinical trial registry, including background information about clinical trials, and a fact sheet detailing the requirements of the 2007 Food and Drug Administration Amendments Act N PLOS Medicine recently launched a Reporting Guidelines Collection, an open access collection of reporting guidelines, commentary, and related research on guidelines from across PLOS journals that aims to help advance the efficiency, effectiveness, and equitability of the dissemination of biomedical information; a 2008 PLOS Medicine editorial discusses the 2007 Food and Drug Administration Amendments Act.
in the published article. The hazard ratio with 95% CI was reported for 68% (15/22) of trials at ClinicalTrials. gov and for 73%(16/22)in the published article. The comparison of efficacy results between ClinicalTrials. govand the published article could not be performed in 96/202 (48%) trials:1. For 42/73 (58%) trials with binary outcomes the comparison could not be made because of incomplete reporting of the number of events. This information was missing in 33 (79%) trials at ClinicalTrials. gov, 25 (60%) in the published article, and 16 (38%) in both data sources. For the 31 trials reporting this information both at ClinicalTrials. gov and in the published article, the numbers of events were the same for 27 (87%).
Acknowledgments We thank  Samuel Dalmeida, M. D. ,  Barouyr Baroudjian, M. D. ,  Camille Hua, M. D. ,  Henri Montaudie, M. D. ,  Christian Derancourt, M. D. ,  Clemence Palazzo ,  M. D. ,  Guillaume Bussone, M. D. , Ph. D , and  Virginie Westeel, M. D. , for independent data extraction.
N ClinicalTrials. gov provides information about the US National Institutes of Health clinical trial registry, including background information about clinical trials, and a fact sheet detailing the requirements of the 2007 Food and Drug Administration Amendments Act N PLOS Medicine recently launched a Reporting Guidelines Collection, an open access collection of reporting guidelines, commentary, and related research on guidelines from across PLOS journals that aims to help advance the efficiency, effectiveness, and equitability of the dissemination of biomedical information; a 2008 PLOS Medicine editorial discusses the 2007 Food and Drug Administration Amendments Act.
in the published article. The hazard ratio with 95% CI was reported for 68% (15/22) of trials at ClinicalTrials. gov and for 73%(16/22)in the published article. The comparison of efficacy results between ClinicalTrials. govand the published article could not be performed in 96/202 (48%) trials:1. For 42/73 (58%) trials with binary outcomes the comparison could not be made because of incomplete reporting of the number of events. This information was missing in 33 (79%) trials at ClinicalTrials. gov, 25 (60%) in the published article, and 16 (38%) in both data sources. For the 31 trials reporting this information both at ClinicalTrials. gov and in the published article, the numbers of events were the same for 27 (87%). 2. For 45/107 (42%) trials with continuous outcomes, the comparison could not be made: nine (20%) because of different types of analysis (e.
g. , the final-value mean [SD] was reported at ClinicalTrials. gov, experimental arm, 1. 12 [0. 54], and control arm, 1. 14 [0. 55], while the change-from-baseline mean [95% CI] was reported in the published article, experimental arm, 0. 59 [0. 47 to 0. 71], and control arm, 0. 54 [0. 41 to 0. 67]), and 36 (80%) because of incomplete or different reporting between the two sources (e. g. , different rounding used; SD reported in one source and standard error [SE] reported in the other). 3. For 9/22 (41%) trials with time-to-event outcomes the comparison could not be made because of incomplete reporting of hazard ratio (95% CI). This information was missing in seven (78%) trials at ClinicalTrials. gov, six (67%) in the published article, and four (44%) in both data sources.
Ripley's  function is the classical tool to characterize the spatial structure of point patterns. It is widely used in vegetation studies. Testing its values against a null hypothesis usually relies on Monte-Carlo simulations since little is known about its distribution. We introduce a statistical test against complete spatial randomness (CSR). The test returns the  value to reject the null hypothesis of independence between point locations. It is more rigorous and faster than classical Monte-Carlo simulations. We show how to apply it to a tropical forest plot. The necessary R code is provided.
Data to Analyze. We consider the distribution of two tree species in Paracou field station, French Guiana  [13]. All trees over 10 cm DBH have been plotted. We use data from a 400. 6 by 522. 3 meters rectangle included in the four plots of the southern block of the experimental device. A map of trees is in Figure  1. Dicorynia guianensis Amsh. is a widely studied species in French Guiana; its spatial structure has been characterized for a long time  [14, 15] ; as a visual inspection of the map allows to guess, Dicorynia guianensis is an aggregated species. Much less is known about Tachigali melinonii (Harms) Barneby. The species has been studied for its special biomechanical behavior  [16]  or leaf trait plasticity  [17]. The spatial structure of its saplings has been reported by Flores et al.
Conclusion Characterizing the spatial structure of a dataset representing the location of plants in an experimental plot is a common task for ecologists  [33]. We provide a rigorous statistical test to reject the null hypothesis that  values of an observed point pattern in a rectangle window are that of a realization of a homogenous Poisson point process. This test replaces advantageously the classical Monte-Carlo one. It will rather complete it in practical applications since Monte-Carlo simulations provide useful local information on the point process. The test is ready to use with the provided R code to be found in the electronic appendices. Future work includes both supporting more complex shapes, probably triangle assemblies following Pélissier and Goreaud  [34] , and other point processes as null hypotheses.
Data In this study, we use three different datasets. The period under review is 2008-2011. The first sample is a panel of "active" bank-firm linkages, corresponding to banks lending a positive amount to firms. It is drawn from a large loan-level sample gathered by the French bank supervisor (the Autorité de Contrôle Prudentiel et de Résolution). This sample allows us to study the effect of capital requirements on the "intensive margin" of lending (i. e. the decision of how much to lend conditional on lending at all). The second sample is a panel of 7 To fix ideas, one can assume that Σ =  2. to simplify the formula. In this case:  =   -. ′ - ′ This formula shows that it is the collinearity between   and  that makes the constraint more binding. active and inactive linkages, i. e. linkages corresponding to firms with potential lenders, with banks lending positive amounts and banks not lending to each firm.
4. b. Firm-level Equation After the bank-firm level analysis, we turn to corporate outcomes. The objective is to understand to what extent the effect of capital requirements on lending can affect the firms' decisions, especially the investment decisions. For this purpose, we use the firm level dataset described in the previous section. We then regress: 𝑌 𝑓,𝑡 = 𝛽 • 𝐾 𝑓,𝑡 + 𝛿 𝑏,𝑡 + 𝛾. 𝑋 𝑓,𝑡 + 𝜀 𝑏,𝑡 (6) where  , can be any corporate outcome (total liabilities, turnover, fixed assets) and  , is the average capital requirement across all the lenders of a given firm. , are firm-level controls such as rating and size dummies. Rating dummies are critical because capital requirements are imputed using actual and potential lenders' identities and firm ratings. Their inclusion ensures that  is identified because different firms face different sets of actual and potential lenders who face different capital requirements.
e. the propensity to lend at all and the amount lent conditional on lending. The other difference between the two datasets is that the balanced panel has only imputed CRs. The use of imputed capital requirements in the balanced panel still leaves us with a lot of identifying power. As we noted in Section 3. b. , imputed CRs have much less variability than true ones. But the model remains identified, even in the presence of firm-year fixed effects.
Selection of meta-analyses One reviewer screened all systematic reviews published in the Cochrane Library in the Cochrane Database of Systematic Reviews (Issue 4, 2011) concerning all medical domains assessing pharmacological therapeutic interventions. Reviews for Cochrane Groups working on pregnancy, neonatality, fertility, cancer childhood, organization care and public health were excluded. Eligible reviews reported at least one meta-analysis of a binary outcome for at least 3 RCTs, of which 1 was specific to older adults (elderly RCT) and 1 not specific to older adults (adult RCT). We focused on Cochrane reviews because some studies have shown that these have high methodological quality and are well reported  [34]. One reviewer selected reviews in 3 steps: examining the title, abstract and full-text for potentially relevant reviews.
Data extraction For each eligible meta-analysis, we extracted the following information from each Cochrane review: clinical domain, experimental and control interventions (placebo, no treatment, active drug), primary binary efficacy outcome, and number of included RCTs. For each RCT, we extracted the age category reported in Table  1  of the Cochrane review, as described: elderly RCT or adult RCT (including RCTs excluding older adults, RCTs of mixed-age adults, unclear RCTs and children-specific RCTs). We also extracted the sample size and the number of patients and events per each study arm from the forest plot of the meta-analysis. If more than 1 primary outcome was reported or if the primary outcome was not specified among several binary outcomes, we selected the first outcome presented in the Results section.
The quality of Cochrane reviews is generally considered very good  [55]. Third, in our main analyses, we included RCTs with ''unspecified/unclear'' age group from each eligible meta-analysis, which accounted for 25% of the trials, but excluding or reclassifying them in sensitivity analyses with adult RCTs yielded similar results. Fourth, the lack of data did not allow for performing formal comparisons of different older age groups (eg, older vs very older). Fifth, the results of both elderly RCTs and adult RCTs may be biased for diverse reasons, and the meta-analysis results may also be affected. Some reviews perform quality and bias assessments, but these assessments can differ across reviews,  [56]  and associations of reported quality with treatment effects may be tenuous  [57]. However, biases are unlikely to differ in elderly RCTs and adult RCTs.
2006; 26). We focus our research on prolific inventors. Previous papers have justified the identification of prolific inventors as those who have been issued at least 15 patents (Le  Bas et al. , 2010; Latham et al. 2011; Latham et al. 2012). In those papers we generally hypothesized that mobility of prolific inventors, as measured by their average numbers of inventions per year over their active inventive lives, affects both their productivity and the value of their inventions, measured as the numbers of citations a patent receives in the years after it is issued, positively. Our previous papers present evidence supporting these hypotheses for the five largest countries in terms of technological activity (the US, Japan, Germany, the UK, and France). Our data come from patents filed by inventors from each of the countries in the US Patent and Trademark Office during the period from 1975 to 2010.
While we focus on the activities of prolific inventors, our data set includes all inventors so the unique characteristics of prolific inventors can be identified. In  Le Bas et al. ( 2010) ,  Latham et al. (2011), and Latham et al. (2012)  we extended the literature to prolific inventors in multiple countries, using alternative indicators for different kinds of mobility. In these papers we measure inventor mobility in two dimensions: across companies ("interfirm mobility") and across regions ("geographic mobility"). For each country we estimate equations for productivity, value and mobility. Our results for the determinants of inventor productivity, mobility and invention value in Germany, France, and the UK show  (Latham et al. 2011 ): 1) In all three countries productivity is positively related to inter-firm mobility and temporal concentration of patenting is also positively related to productivity.
Data, Variables and Models Data Our data are from the NBER Patent Data Base (http://www. nber. org/patents/) which contains data for more than 5million patents granted to more than 2 million inventors by the USPTO from 1975 to 2010. For this paper we extract data for patents issued to inventors from eleven countries. For each patent we obtain the application and grant dates, the inventor's name and city of residence), assignee name and location, the US and international technical classifications, citations of prior patents, and the number of separate technical claims the patent makes. The data are compiled for individual inventors; we focus on as the prolific inventors, those who with at least 15 patents 2.
Control Variables In our dataset we observe that there are some inventors with careers of patenting that span many years and others whose patents are all produced in a very short period. To account for this variation we measure the duration of an inventor's career (years from first to last invention, inclusive= CAREER_DURATION). We wish to control for another phenomenon happening through an inventor career. When we look at the data we observe that inventors do not invent continuously. They seem interrupt the invention activity their activity over a more or less long time period. The variable is measured as the maximum number of years between two consecutive patent applications: CAREER_TIME_GAP. We also observe that the career patterns of inventing are highly variable from prolific inventor to prolific inventor with some inventors having most patents at the beginning, some having most at the end, some showing a pattern of increase followed by decrease and still others having multimodal distributions.
Data The raw datasets are obtained from three observatories: Chambon-La-Forêt (France), Hermanus (South Africa) and Thule (Denmark). The first two observatories are representative of the mid-latitude geomagnetic field variations, and the third is chosen for a comparison with a higher latitude station. Additionally, data from the Hermanus observatory are used together with data from 3 other mid-latitude observatories for the calculation of the Dst index. The geomagnetic coordinates of these observatories computed using the IGRF 2005 are given in Table  1. These datasets have been either downloaded from the World Data Center for Geomagnetism in  Kyoto (swdcwww. kugi. kyoto-u. ac. jp)  or the INTERMAGNET website (www. intermagnet. org/apps/dl data def f. php). We focus on the horizontal component H of the geomagnetic field, as this is mainly affected by the magnetospheric dynamics.
However, here we show the results obtained for the CLF observatory, only. Second, let us note that ten large geomagnetic storms (Dst < -100 nT) occurred near the solar maximum period of solar cycle 23. From these events, we select six geomagnetic storms which are investigated in more detail: 15  May, 1997 , 14 July, 2000 , 3-7 September, 2002 , 28-29 October, 2003 , 20 November, 2003 and 7-9 November, 2004. The main characteristics of these events are summarized in Table  2. The selected time intervals include the geomagnetic disturbance and a previous period of relatively-low geomagnetic activity. In order to develop this analysis, one-minute sampling data provided at the three magnetic observatories are used. This sampling rate is necessary for a detailed analysis. Quiet and disturbed days are separately considered in the analysis.
Extended analysis 2001 Magnetic Storms: One-year window For a detailed analysis using hourly-mean values, we have examined the geomagnetic time series recorded at the CLF, HER, and THL observatories during the year 2001, characterized by two important events. Our goal is now to make a comparative study of the wavelet spectral exponent deduced from the geomagnetic time series with the Dst index scaling behaviour studied by  Balasis et al. (2006). We would like to note that this study deals with the analysis of raw time series recorded in geomagnetic observatories. In this context, the used data have not been preprocessed (e. g. filtered) prior to the wavelet analysis. Therefore, the scaling exponent values may fluctuate from the values specifically known for the quiet periods (i. e. , when 1 < β(t) < 2 is expected) or for the disturbed periods (i.