dataesr/scientific-paragraphs-categorization huggingface dataset:
Bulletin TABLE D2 Changes in inequality (I 2 ) decomposed by income source, 95% confidence intervals Note: Confidence intervals are calculated by creating 1,199 pseudo-samples of the entire time-series of data, and carrying out the decomposition on each of these pseudo-samples. See also note for Table  2. Source: Authors' calculations using Family Expenditure Survey and Family Resources Survey, various years, as described in Appendix D. Note: Confidence intervals are calculated by creating 1,199 pseudo-samples of the entire time-series of data, and carrying out the decomposition on each of these pseudo-samples. See also note for Table  3. Source: Authors' calculations using Family Expenditure Survey and Family Resources Survey, various years, as described in Appendix D. 2007-08), and it is adjusted at the top, with the HBAI series giving most of the richest 1% an imputed value of income derived from administrative data on incomes and tax payments.
We use the underlying FES/FRS micro data sets to provide us with data on individual earnings. We have a measures of the tax paid on employment income, but only at the household level. We also use data from the FRS to estimate the amount of benefit income that is received in the form of tax credits from 2000-01 onwards, which we treat (differently from the official HBAI series) as an income source in its own right. Benefit income is then treated separately according to the type of household that receives the income; in particular, we identify households which are headed by a pensioner, and households which include children. As different benefits are targeted at different types of household, this allows us to approximate which kind of benefits are driving our results. For example, though we cannot distinguish directly between income received from public pensions and other benefits, we can infer that pensions are likely to be responsible for any change observed among benefits received by pensioners.
We also use data on self-employment and investment income. For both, we might be concerned with the quality of the data given the known difficulties in collecting accurate income from the self-employed and from those with a large number of sources of investment income. To the extent that any inaccuracies simply adds noise to the data, this will be picked up in the confidence intervals generated by bootstrapping. Systematic bias may also be a problem, but this would only be a major concern if we believed that such bias was likely to have changed significantly over the period analysed. We also tried including two additional variables for those individuals in work -their occupation and the industry in which they worked. However, we do not have consistent series of either variable: the Family Expenditure Survey changed its occupation categories in 1987, the Family Resources Survey changed its occupation categories in 2001-02, and there are no data on industrial sectors in the Family Expenditure Survey after 1986 (the ONS published a mapping from the pre-2001 to the post-2001 occupation categories in the FRS, but we find that it gives highly inconsistent results and so we do not use it in our analysis).
The patients are eventually included in the appropriate RCT when all eligibility criteria are met. The present study proposes to retrospectively analyze the decision-making process of the MDM and to compare it to a systematic review of the patient's case. Focus on the ongoing randomized controlled trials used for the study Urologic oncology was chosen as the experimental field for practical reasons, partly because most eligibility criteria are readily handled by computerized data (Tumor, lymph node and metastasis classification of malignant tumors (TNM) staging, the Gleason score, prostatespecific antigen (PSA) level, and so forth). The study focused on prostate cancer, which is the most common form in urology. It is also the main topic of discussion at urology MDMs and of many clinical trials.
Four prostate cancer clinical trials were active and recruiting patients at the Cancer Research Institute of Rennes during the study period (between October 2008 and March 2009), all conducted by the French Genitourinary Tumor Group (GETUG)  [8] : GETUG 14, comparing triptorelin, flutamide, and external-beam radiation therapy to external-beam radiation therapy alone in treatment of patients with stage II or stage III prostate cancer (Clinicaltrials. gov identifier: NCT00104741); GETUG 15, comparing hormone therapy and docetaxel to hormone therapy alone in treatment of patients with metastatic prostate cancer (Clinicaltrials. gov identifier: NCT00104715); GETUG 16, comparing radiation therapy and goserelin to radiation therapy alone in treatment of patients who have undergone surgery for recurrent or refractory prostate cancer (Clinicaltrials.
gov identifier: NCT00423475); GETUG 17, comparing immediate triptorelin and radiation therapy after surgery to the delayed treatment of patients who have undergone surgery for intermediate-risk stage III or stage IV prostate cancer (Clinicaltrials. gov identifier: NCT00667069). Detailed eligibility criteria for the four RCTs discussed at the MDMs during the study period are presented in Table  1. The RCTs were conducted in accordance with the Declaration of Helsinki. All participants were required to give written informed consent before enrollment in one of the four studies.
Data collection The data used in our study consisted of the information contained in the MDC reports, and of the corresponding therapeutic decisions taken during the MDMs. The MDC reports included general characteristics of patients, medical history, and data related to prostate cancer, such as the Gleason score, tumor staging (clinical and pathological TNM classification), and the prostatespecific antigen (PSA) level. According to each case, the MDC team's decisions were either to propose a standard therapy to the patient, or to participate in a clinical trial (in this case, the MDC team had estimated that pre-.
All four trials cited in this study obtained ethical approval by a research ethics committee with the following approval numbers for GETUG 14: 2003/015 (CPPRB Haute Normandie), for GETUG 15: 04/08 (CPPRB Marseille 1), GETUG 16: 06/025 (CPPRB Lyon), GETUG 17: 2007/53 (CPP Sud-Ouest et Outre Mer III). Eligibility GETUG 14 (NCT00104741) GETUG 15 (NCT00104715) GETUG 16 (NCT00423475) GETUG 17 (NCT00667069) criteria Disease Histology Histologically confirmed prostate Histologically confirmed prostate Histologically confirmed prostate Histologically confirmed prostate characteristics cancer: (Stage T1b-T1c AND PSA adenocarcinoma a adenocarcinoma: pT2, pT3, or pT4, adenocarcinoma: pT3a, pT3b ≥10 ng/mL) OR (Stage T1b-T1c T2a-T3a) a AND Gleason score ≥7 OR Stage pN0 or pNx a (or pT4 by reaching the bladder neck), or R1 disease, OR pN0 or pNx a Disease's prior Not specified Not specified Treated with surgery only a Treated by curative surgery in the treatment past 6 months a Positive margins (tumoral glands in contact with contour ink) Metastasis No metastatic disease (M0) Metastatic disease a Not specified Not specified confirmed by thoracic radiography and bone scan a Disease's History of invasive cancer a Brain metastases a Clinical signs of progressive disease pN1 disease, pT2 disease, exclusion criteria Lymph node invasion (N0 or N-) a Other histology than adenocarcinoma a PSA/GLEASON score PSA <30 ng/mL Not specified PSA ≤0.
The CAPRI N-LCA approach The emissions of GHGs (N 2 O and CH 4 ) and reactive nitrogen (N r ) to air (NH 3 , NO x ) and water (N leaching and runoff) and other N flows (such as trade of food and feed) were estimated with CAPRI, using the cradle-to-gate LCA approach implemented for the estimation of GHG emissions (Weiss and Leip 2012) and further extended to include the estimation of N footprints per product group (Leip et al 2014b). Here we present new data that have been calculated when further extending the model with flows of all N r. All new data presented here include emissions occurring within Europe and outside the EU territory for imported feed and/or land use change (LUC; LUC emissions were calculated using the land use transition probabilities of scenario II in Weiss and Leip (2012), table  A1 ).
Soil acidification Contribution to soil acidification was assessed on the basis of the sum of SO 2 , NH 3 and NO x emissions. NH 3 and NO x emissions were calculated with CAPRI N-LCA. Total SO 2 emissions were obtained from the EDGAR data base (European Commission 2011). SO 2 emissions caused by agricultural activities were approximated by the ratio of CO 2 emissions from agriculture related to energy use (Weiss and Leip 2012) and total energy CO 2 emissions in EU27 (EEA 2011). This gives a share of about 6%, which is associated with livestock products (4%) and vegetable products (2%), in accordance with the global estimate of FAO  (FAO 2006). The contribution of agricultural sources to emissions of acidifying substances was estimated on the basis of acidity equivalents  (Schöpp and Posch 2003).
Terrestrial biodiversity The contribution of agriculture, livestock and feed to loss of relative mean species abundance (MSA) was estimated using shares of land use and emissions of NH 3 , NO x , and net GHG exchange, accounting for C sequestration, as calculated with the CAPRI-LCA. The data are linked to the estimates of the effect of the main drivers for biodiversity loss as calculated with the GLOBIO-model  (Alkemade et al 2009 , Kram and Stehfest 2012 , van Vuuren et al 2015). This model gives an absolute loss of 65% MSA caused by land conversion into arable, grazing and forestry (35%, 15% and 14%, respectively), and to pressures such as N deposition (2%), climate change (3%) and land fragmentation (30%) (for details see supplementary information  S3 ).
Phosphorus Diffuse losses of DIP from agriculture were estimated at 0. 025 Tg P yr -1 , while weathering in agricultural systems contributes an estimated additional 0. 003 Tg P yr -1 (Mayorga et al 2010). By far the largest share of net P input (P input minus P crop removal) was retained in the soil, which is considered a benefit as long as this leads to increased soil fertility, i. e. , with low erosion. We do not have an estimate of agricultural dissolved organic phosphorus (DOP) or PP, as it is very difficult to distinguish sources for DOP and particularly for PP export. However, most likely the contribution of agriculture is much higher for PP than for DIP while PP dominates P export. The data presented in table 2 relate to DIP only and are therefore to be regarded as a conservative estimate for the total contribution of agriculture to P flows to coastal areas in Europe.
Uncertainty of emission estimates For N, combining NH 3 +NO x emissions, our estimate of 2. 6 Tg N yr  countries, while national inventories in Europe are constructed with a large variety of methods and data quality (Leip 2010). National estimates would be of higher quality than CAPRI estimates from countries with good data (Leip et al 2014b), but some countries still need to improve their methodology (EEA 2014). Furthermore, CAPRI uses ammonia abatement measures from the GAINS model (Klimont and Winiwarter 2011) which may not have been considered in national inventories (Leip et al 2010). In comparison with 4. 4 Tg N yr -1 agricultural NH 3 emissions in the EDGAR data base, our estimate of 2. 6 Tg N yr -1 for EU27, excluding emissions from imported feed, is lower. The reason for this might be the lower excretion estimates, although the NH 3 emissions are in line with estimates by the MITERRA model.
In our study the estimated N deposition was larger (0. 82 Tg N yr -1 ) while the C:N response was estimated at 35 kg C per kg N deposited. Estimates of agricultural N-leaching range from 2. 0 to 5. 7 Tg N yr -1 (de Vries et al 2011b), the higher value being also found in the EU GHG inventory (EEA 2014). Possible reasons for these differences-in addition to those already discussed-are available calibration data for nitrate concentrations which might neglect flows of organic nitrogen to water, and the split of total N between the highly uncertain N 2 emissions and N leaching/runoff (de Vries et al 2011b). Our estimate of nitrate leaching at 5. 4 Tg N yr -1 is consistent with the estimate of the European Nitrogen Budget (Leip et al 2011a) which is used in table 3 for total N input to water. Although livestock dominated overall agriculture P flows (73%), agriculture is responsible for only 10% of the total riverine P export (table  3 ).
This value is obtained from statistical sources (market balances) and is further constrained by energy and nutrient requirement calculations for major livestock types. No data are available whether farmers prefer domestically produced crops or imported crops for feed; therefore this value is highly uncertain. Because of the lack of information, we considered equal preference. However, this uncertainty concerns only primary crops that are used for both food and feed, which make only 12% of the total feed, while the rest comes from non-marketable crops (82%), such as grass, fodder maize and beet, or secondary crops (6%). Nonmarketable crops are all domestically produced; secondary feed stuff is dominated by imported soya bean.
Acknowledgments The authors would like to thank the  European Commission  funded research projects  NitroEurope-IP  ( FP6-017841 ),  ANIMALCHANGE  ( FP7-266018 ), and  ECLAIRE  ( FP7-282910 ) for supporting the research and collaboration underpinning the results presented in this paper. We also like to thank  Dr Jean Philippe Putaud  ( EC-JRC ) for helpful discussion on the aerosol phenomenology and  Dr Faycal Bouraoui  ( EC-JRC ) for discussion on the results of the model GREEN.
Latin America and the Caribbean is recognized as a region of entrepreneurs, as evidenced by the high amount of ventures per capita; however, just a few number of companies export and most of them show a weak growth and little innovation. Using information from the GEM database -Colombia 2010 and 2011, this study investigates the mutual effects between the owner-manager's commitment to innovation and the export behavior of companies. Logistic and Ordinal regression models are used to test the proposed hypotheses in this study. The main findings of this study suggest that the owner-manager's commitment to innovation is an important booster for exports, as well as for increasing sales abroad. Furthermore, the results suggest that the export propensity, in turn, increases the likelihood of involvement of owner-managers to innovate in Colombia.
al. , 2014) , there are relatively few studies about the background and the driving forces of the innovative capability  (Hult, et. al. , 2004). According to the Resources and Capabilities-Based Theory  (Barney, 1991; Teece, et. al. , 1997) , as well as the Learning Theory  (Cohen and Levinthal, 1990; Lane and Lubatkin, 1998) , an analysis which considers the mutual relationship between the owner-manager's commitment to innovation and the export behavior of the company, is suggested. Consequently, this research aims to provide empirical evidences that support the comprehension on this phenomenon in the region, and which contributes to both the academia and policy makers. This article is organized with the following structure: the second section shows the theoretical framework, with a brief description of previous studies and the proposed hypothesis; the third section specifies the research methodology and describes the database; the fourth section presents the estimation of results; and the final section provides a discussion about the findings and conclusions.
Database In this study we use data from the Global Entrepreneurship Monitor (GEM) adult population survey (APS) collected between 2010 and 2011 in Colombia. The GEM research program is an annual assessment of the national levels of entrepreneurial and business activity. Initiated in 1999 it is the single largest study of entrepreneurial activity in the world cumulating nearly 100 national research teams as of 2014. The main advantage of GEM database is the fairly large sample size, in addition to consistency in definition and measures across multiple contexts. Thus, in addition to its external validity the use of GEM dataset allows understanding business and entrepreneurial activity across time and space (see  Reynolds, et. al. , 2005). In this study, the units of analysis are individuals who are leading an already established organization (considering only those individuals who own or run a business at the time the survey was done).
This article sheds light on a quantitative method allowing psychologists and behavioral scientists to take into account the specific characteristics emerging from the interaction between two sets of data in general and two individuals in particular. The current article outlines the practical elements of the cross-wavelet transform (CWT) method, highlighting WHY such a method is important in the analysis of time-series in psychology. The idea is (1) to bridge the gap between physical measurements classically used in physiologyneuroscience and psychology; (2) and demonstrates how the CWT method can be applied in psychology. One of the aims is to answer three important questions WHO could use this method in psychology, WHEN it is appropriate to use it (suitable type of time-series) and HOW to use it.
Throughout these explanations, an example with simulated data is used. Finally, data from real life application are analyzed. This data corresponds to a rating task where the participants had to rate in real time the emotional expression of a person. The objectives of this practical example are (i) to point out how to manipulate the properties of the CWT method on real data, (ii) to show how to extract meaningful information from the results, and (iii) to provide a new way to analyze psychological attributes.
INTRODUCTION In psychology, numerous studies investigate the interactions between different components of a living system or between different living systems. The idea of interaction is meant here as the expression of action that occurs as two or more 'objects' have an effect upon one another  (Sebanz and Knoblich, 2009). Numerous studies in psychology also investigate the progression in time of these interactions  (Boyd et al. , 2011). The data set obtained when analyzing human interaction, and even more when analyzing the temporal evolution of human interaction, contains a lot of powerful information, which is sometimes difficult to quantify and interpret. The cross-wavelet 1 transform (CWT) method offers a way to overcome such difficulties. The 'cross' WT method is a technique that characterizes the interaction between the wavelet transform (WT) of two individual time-series.
The CWT method allows us (i) to measure the degree of synchronization phenomenon between different components and conjointly (ii) the evolution over time of the studied phenomenon (i. e. , times-series data set). For example, it allows the extraction of information about how a behavioral state changes, how long it takes to progress from one state to another and how many states occur in a given time period. In addition, similarity/differences between two time-series (e. g. , the similarity between two people's behavioral states) can be identified. If these behavioral states do not have a similar dynamic (i. e. , different temporal evolution), the CWT method helps quantifying how different they are, and what is the time lag between the two different behavioral states? In short, this method is able to yield information on the spatio-temporal organization between two time-series, or in other words how two time-series evolve one against the other.
Similar observation can be done in interval 5. On the high frequency component (1 Hz), there is a RP of 180 • (white color) and on the intermediate frequency component (0. 75 Hz) a RP of 90 • (light gray color). This example illustrates that the CWT method can detect the phase synchronization that could occur in plurifrequential time-series (i. e. , several time-scales occurring at the same time). These illustrations are mostly based on a visual description of Figure  4. The next step is to understand how to extract data from the time-frequency representation (for instance from Figures  4D, F, H, I ).
Then some significant data could be skipped. We suggest using more flexible methods to extract the information. In the second method, one unique value is taken out of the CWT spectrum that corresponds to the highest (or maximum) cross-modulus value (in the significant area) for each time step, also called the crest line. This value corresponds to the time and frequency where the two time-series show a high degree of interaction. In such a case, the extraction of RP values is required to match up the corresponding index (time and frequency) of crossmodulus spectrogram on the cross-phase spectrogram. Hence, if we are obtaining a crest line, for example, at the 31st second at 1. 04 Hz from Figure  4H , the corresponding RP has to be extracted in Figure  4I  from the same time: 31st second and at 1.
INTRODUCTION The aim of this section is to illustrate the practical use of the WT and CWT presented above. As illustrated previously, the CWT method can be applied to different kinds of time-series (see section "When"). The current set of data has been obtained from the portal page of the European Network of Excellence called HUman-MAchine Interaction Network on Emotions (i. e. , HUMAINE4). The purpose of the HUMAINE Network of Excellence concerned the development of systems that can "register, model, and/or influence human emotional and emotion related-states and processes" as can be read in the project description 4. Part of the research program involved the development of the set of TRACE programs (Queen's University Belfast, Belfast, UK). The TRACE programs let observers track the emotional content of a stimulus as they perceive it over time.
METHODS Forty eight video clips are available from the HUMAINE database. These clips show emotion in action in different contexts  (static, dynamic, indoor, monolog, and dialog). For the purpose of this practical example, we randomly selected one of these clips for the analysis. The clip shows a male participant being interviewed about his experience on a reality survival show. The selected video clip has a duration of 119 s. Ten participants were recruited to rate the video clip (five women and five men). The TRACE program was set up at a sampling rate of 10 Hz. The raters watched this video and performed the rating task to rate the emotional state of the participant. From this set of TRACE programs, one obtains a set of data where the amplitude of the trace illustrates the rise and fall of the rater "traces.
Such beyond-chance associations among diseases may occur when one disease is directly responsible for others (i. e. , complicating multimorbidity) or when several diseases share common or correlated risk factors (i. e. , causal multimorbidity), which may be biological, socio-economic, cultural, environmental or behavioural in nature  [8]. In both cases, the potential for secondary and tertiary prevention is high, underscoring the importance of these types of studies. To date, the majority of studies of multimorbidity patterns have been limited to chronic diseases, so as to increase the likelihood of co-occurrence of conditions  [9]. Despite their clinical relevance, very few studies have included geriatric syndromes in their analyses, probably due to the limited availability of relevant data in existing patient databases.